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ORCIDTim Menzies
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- affiliation: North Carolina State University, North Carolina, USA
- affiliation (former): University of British Columbia, Vancouver, Canada
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2020 – today
- 2024
[j161]Xiao Ling
, Tim Menzies, Christopher J. Hazard, Jack Shu, Jacob Beel:
Trading Off Scalability, Privacy, and Performance in Data Synthesis. IEEE Access 12: 26642-26654 (2024)- [j160]Andre Lustosa, Tim Menzies:
iSNEAK: Partial Ordering as Heuristics for Model- Based Reasoning in Software Engineering. IEEE Access 12: 142915-142929 (2024) - [j159]Suvodeep Majumder, Joymallya Chakraborty, Tim Menzies:
When less is more: on the value of "co-training" for semi-supervised software defect predictors. Empir. Softw. Eng. 29(2): 51 (2024) - [j158]Tim Menzies:
A brief note, with thanks, on the contributions of Guenther Ruhe. Inf. Softw. Technol. 173: 107486 (2024) - [j157]Tim Menzies, Bowen Xu, Hong Jin Kang, Jie M. Zhang, Jiri Gesi, Sagar Sen, Beatriz Cassoli, Nicolas Jourdan, Jieke Shi, Phu Hong Nguyen, Valentina Golendukhina:
SEA4DQ 2024 Workshop Summary. ACM SIGSOFT Softw. Eng. Notes 49(4): 29-30 (2024) - [j156]Brittany Johnson, Tim Menzies:
Ethics: Why Software Engineers Can't Afford to Look Away. IEEE Softw. 41(1): 142-144 (2024) - [j155]Brittany Johnson, Tim Menzies:
Fighting for What's Right: An Interview With Marc Canellas. IEEE Softw. 41(2): 104-107 (2024) - [j154]Brittany Johnson, Tim Menzies:
The Power of Positionality - Why Accessibility? An Interview With Kevin Moran and Arun Krishnavajjala. IEEE Softw. 41(3): 91-94 (2024) - [j153]Brittany Johnson, Tim Menzies:
Are You Trapped in the Configuration Abyss? An Interview With Prof. Sven Apel. IEEE Softw. 41(4): 175-181 (2024) - [j152]Tim Menzies, Brittany Johnson:
Powering Down: An Interview With Federica Sarro on Tackling Energy Consumption in AI-Powered Software Systems. IEEE Softw. 41(5): 89-92 (2024) - [j151]Brittany Johnson, Tim Menzies:
AI Over-Hype: A Dangerous Threat (and How to Fix It). IEEE Softw. 41(6): 131-138 (2024) - [j150]Andre Lustosa, Tim Menzies:
Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health. ACM Trans. Softw. Eng. Methodol. 33(3): 58:1-58:22 (2024) - [j149]Zhe Yu, Joymallya Chakraborty, Tim Menzies:
FairBalance: How to Achieve Equalized Odds With Data Pre-Processing. IEEE Trans. Software Eng. 50(9): 2294-2312 (2024) - [e12]Tim Menzies, Bowen Xu, Hong Jin Kang, Jie M. Zhang:
Proceedings of the 4th International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things, SEA4DQ 2024, Porto de Galinhas, Brazil, 15 July 2024. ACM 2024 [contents] - [i118]Md. Rayhanur Rahman, Brandon Wroblewski, Quinn Matthews, Brantley Morgan, Tim Menzies, Laurie A. Williams:
Mining Temporal Attack Patterns from Cyberthreat Intelligence Reports. CoRR abs/2401.01883 (2024) - [i117]Rahul Yedida, Tim Menzies:
SMOOTHIE: A Theory of Hyper-parameter Optimization for Software Analytics. CoRR abs/2401.09622 (2024) - [i116]Tim Menzies, Andre Lustosa:
Streamlining Software Reviews: Efficient Predictive Modeling with Minimal Examples. CoRR abs/2405.12920 (2024) - 2023
- [j148]Maria Teresa Baldassarre, Neil A. Ernst, Ben Hermann, Tim Menzies, Rahul Yedida:
(Re)Use of Research Results (Is Rampant). Commun. ACM 66(2): 75-81 (2023) - [j147]Kewen Peng, Christian Kaltenecker, Norbert Siegmund, Sven Apel, Tim Menzies:
VEER: enhancing the interpretability of model-based optimizations. Empir. Softw. Eng. 28(3): 61 (2023) - [j146]Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic:
An expert system for redesigning software for cloud applications. Expert Syst. Appl. 219: 119673 (2023) - [j145]Guanqin Zhang, Jiankun Sun, Feng Xu, Yulei Sui, H. M. N. Dilum Bandara, Shiping Chen, Tim Menzies:
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning. IEEE Internet Comput. 27(6): 13-20 (2023) - [j144]Tim Menzies, Brittany Johnson, David L. Roberts, Lauren Alvarez:
The Engineering Mindset Is an Ethical Mindset (We Just Don't Teach It That Way... Yet). IEEE Softw. 40(2): 103-110 (2023) - [j143]Lauren Alvarez, Tim Menzies:
Don't Lie to Me: Avoiding Malicious Explanations With STEALTH. IEEE Softw. 40(3): 43-53 (2023) - [j142]Tim Menzies:
How to "Sell" Ethics (Using AI): An Interview With Alexander Serebrenik. IEEE Softw. 40(3): 95-97 (2023) - [j141]Tim Menzies, Chris Hazard:
"The Best Data Are Fake Data?": An Interview With Chris Hazard. IEEE Softw. 40(5): 121-124 (2023) - [j140]Brittany Johnson, Tim Menzies:
Unfairness Is Everywhere, so What to Do? An Interview With Jeanna Matthews. IEEE Softw. 40(6): 135-138 (2023) - [j139]N. C. Shrikanth, Tim Menzies:
Assessing the Early Bird Heuristic (for Predicting Project Quality). ACM Trans. Softw. Eng. Methodol. 32(5): 116:1-116:39 (2023) - [j138]Suvodeep Majumder, Joymallya Chakraborty, Gina R. Bai, Kathryn T. Stolee, Tim Menzies:
Fair Enough: Searching for Sufficient Measures of Fairness. ACM Trans. Softw. Eng. Methodol. 32(6): 134:1-134:22 (2023) - [j137]George Mathew, Amritanshu Agrawal, Tim Menzies:
Finding Trends in Software Research. IEEE Trans. Software Eng. 49(4): 1397-1410 (2023) - [j136]Kewen Peng, Joymallya Chakraborty, Tim Menzies:
FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes. IEEE Trans. Software Eng. 49(4): 2426-2439 (2023) - [j135]Rahul Yedida, Hong Jin Kang, Huy Tu, Xueqi Yang, David Lo, Tim Menzies:
How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs. IEEE Trans. Software Eng. 49(4): 2856-2872 (2023) - [j134]Xiao Ling, Tim Menzies:
What Not to Test (For Cyber-Physical Systems). IEEE Trans. Software Eng. 49(7): 3811-3826 (2023) - [c167]Tim Menzies:
Model Review: A PROMISEing Opportunity. PROMISE 2023: 64-68 - [d2]Tim Menzies:
4src/fish: another release to make zenodo to index this site. Version v0.8.0. Zenodo, 2023 [all versions]
- [d1]Tim Menzies:
4src/fish: another release to make zenodo to index this site. Version v0.9.01. Zenodo, 2023 [all versions] - [i115]Andre Lustosa, Tim Menzies:
Optimizing Predictions for Very Small Data Sets: a case study on Open-Source Project Health Prediction. CoRR abs/2301.06577 (2023) - [i114]Lauren Alvarez, Tim Menzies:
Don't Lie to Me: Avoiding Malicious Explanations with STEALTH. CoRR abs/2301.10407 (2023) - [i113]Huy Tu, Tim Menzies:
Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics. CoRR abs/2302.01997 (2023) - [i112]Xiao Ling, Tim Menzies:
On the Benefits of Semi-Supervised Test Case Generation for Cyber-Physical Systems. CoRR abs/2305.03714 (2023) - [i111]Tim Menzies:
Model Review: A PROMISEing Opportunity. CoRR abs/2309.01314 (2023) - [i110]Xueqi Yang, Mariusz Jakubowski, Kelly Kang, Haojie Yu, Tim Menzies:
SparseCoder: Advancing Source Code Analysis with Sparse Attention and Learned Token Pruning. CoRR abs/2310.07109 (2023) - [i109]Andre Lustosa, Tim Menzies:
Partial Orderings as Heuristic for Multi-Objective Model-Based Reasoning. CoRR abs/2310.19125 (2023) - [i108]Xiao Ling, Tim Menzies, Christopher J. Hazard, Jack Shu, Jacob Beel:
Trading Off Scalability, Privacy, and Performance in Data Synthesis. CoRR abs/2312.05436 (2023) - 2022
- [j133]Atif Mashkoor, Tim Menzies, Alexander Egyed, Rudolf Ramler:
Artificial Intelligence and Software Engineering: Are We Ready? Computer 55(3): 24-28 (2022) - [j132]Rui Shu, Tianpei Xia, Laurie A. Williams, Tim Menzies:
Omni: automated ensemble with unexpected models against adversarial evasion attack. Empir. Softw. Eng. 27(1): 26 (2022) - [j131]Suvodeep Majumder, Pranav Mody, Tim Menzies:
Revisiting process versus product metrics: a large scale analysis. Empir. Softw. Eng. 27(3): 60 (2022) - [j130]Huy Tu, Tim Menzies:
DebtFree: minimizing labeling cost in self-admitted technical debt identification using semi-supervised learning. Empir. Softw. Eng. 27(4): 80 (2022) - [j129]Tianpei Xia, Wei Fu, Rui Shu, Rishabh Agrawal, Tim Menzies:
Predicting health indicators for open source projects (using hyperparameter optimization). Empir. Softw. Eng. 27(6): 122 (2022) - [j128]Sarah Elder, Nusrat Zahan, Rui Shu, Monica Metro, Valeri Kozarev, Tim Menzies, Laurie A. Williams:
Do I really need all this work to find vulnerabilities? Empir. Softw. Eng. 27(6): 154 (2022) - [j127]Zhe Yu, Jeffrey C. Carver, Gregg Rothermel, Tim Menzies:
Assessing expert system-assisted literature reviews with a case study. Expert Syst. Appl. 200: 116958 (2022) - [j126]Nelly Bencomo, Jin L. C. Guo, Rachel Harrison, Hans-Martin Heyn, Tim Menzies:
The Secret to Better AI and Better Software (Is Requirements Engineering). IEEE Softw. 39(1): 105-110 (2022) - [j125]Huy Tu, Zhe Yu, Tim Menzies:
Better Data Labelling With EMBLEM (and how that Impacts Defect Prediction). IEEE Trans. Software Eng. 48(2): 278-294 (2022) - [j124]Zhe Yu, Fahmid Morshed Fahid, Huy Tu, Tim Menzies:
Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach. IEEE Trans. Software Eng. 48(5): 1676-1691 (2022) - [j123]Tianpei Xia, Rui Shu, Xipeng Shen, Tim Menzies:
Sequential Model Optimization for Software Effort Estimation. IEEE Trans. Software Eng. 48(6): 1994-2009 (2022) - [j122]Kewen Peng, Tim Menzies:
Defect Reduction Planning (Using TimeLIME). IEEE Trans. Software Eng. 48(7): 2510-2525 (2022) - [j121]Xiao Ling, Rishabh Agrawal, Tim Menzies:
How Different is Test Case Prioritization for Open and Closed Source Projects? IEEE Trans. Software Eng. 48(7): 2526-2540 (2022) - [j120]Amritanshu Agrawal, Xueqi Yang, Rishabh Agrawal, Rahul Yedida, Xipeng Shen, Tim Menzies:
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When? IEEE Trans. Software Eng. 48(8): 2939-2954 (2022) - [j119]Rahul Yedida, Tim Menzies:
On the Value of Oversampling for Deep Learning in Software Defect Prediction. IEEE Trans. Software Eng. 48(8): 3103-3116 (2022) - [c166]Rui Shu, Tianpei Xia, Laurie A. Williams, Tim Menzies:
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue. MSR 2022: 144-155 - [c165]Rahul Yedida, Tim Menzies:
How to Improve Deep Learning for Software Analytics (a case study with code smell detection). MSR 2022: 156-166 - [c164]Suvodeep Majumder, Tianpei Xia, Rahul Krishna, Tim Menzies:
Methods for Stabilizing Models Across Large Samples of Projects (with case studies on Predicting Defect and Project Health). MSR 2022: 566-578 - [i107]Huy Tu, Tim Menzies:
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning. CoRR abs/2201.10592 (2022) - [i106]Rahul Yedida, Tim Menzies:
How to Improve Deep Learning for Software Analytics (a case study with code smell detection). CoRR abs/2202.01322 (2022) - [i105]Rui Shu, Tianpei Xia, Laurie A. Williams, Tim Menzies:
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue. CoRR abs/2203.11410 (2022) - [i104]Rui Shu, Tianpei Xia, Huy Tu, Laurie A. Williams, Tim Menzies:
Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning). CoRR abs/2205.00665 (2022) - [i103]Rahul Yedida, Hong Jin Kang, Huy Tu, Xueqi Yang, David Lo, Tim Menzies:
How to Find Actionable Static Analysis Warnings. CoRR abs/2205.10504 (2022) - [i102]Sarah Elder, Nusrat Zahan, Rui Shu, Monica Metro, Valeri Kozarev, Tim Menzies, Laurie A. Williams:
Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application. CoRR abs/2208.01595 (2022) - [i101]Suvodeep Majumder, Joymallya Chakraborty, Tim Menzies:
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors. CoRR abs/2211.05920 (2022) - [i100]Guanqin Zhang, Jiankun Sun, Feng Xu, H. M. N. Dilum Bandara, Shiping Chen, Yulei Sui, Tim Menzies:
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning. CoRR abs/2211.10012 (2022) - 2021
- [j118]Rui Shu, Tianpei Xia, Jianfeng Chen, Laurie A. Williams, Tim Menzies:
How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization). Empir. Softw. Eng. 26(3): 53 (2021) - [j117]Xueqi Yang, Jianfeng Chen, Rahul Yedida, Zhe Yu, Tim Menzies:
Learning to recognize actionable static code warnings (is intrinsically easy). Empir. Softw. Eng. 26(3): 56 (2021) - [j116]N. C. Shrikanth, William Nichols, Fahmid Morshed Fahid, Tim Menzies:
Assessing practitioner beliefs about software engineering. Empir. Softw. Eng. 26(4): 73 (2021) - [j115]Xueqi Yang, Zhe Yu, Junjie Wang, Tim Menzies:
Understanding static code warnings: An incremental AI approach. Expert Syst. Appl. 167: 114134 (2021) - [j114]Tim Menzies:
Shockingly Simple: "KEYS" for Better AI for SE. IEEE Softw. 38(2): 114-118 (2021) - [j113]Junjie Wang, Song Wang, Jianfeng Chen, Tim Menzies, Qiang Cui, Miao Xie, Qing Wang:
Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing. IEEE Trans. Software Eng. 47(6): 1259-1276 (2021) - [j112]Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen, Tim Menzies:
How to "DODGE" Complex Software Analytics. IEEE Trans. Software Eng. 47(10): 2182-2194 (2021) - [j111]Zhe Yu, Christopher Theisen, Laurie A. Williams, Tim Menzies:
Improving Vulnerability Inspection Efficiency Using Active Learning. IEEE Trans. Software Eng. 47(11): 2401-2420 (2021) - [j110]Rahul Krishna, Vivek Nair, Pooyan Jamshidi, Tim Menzies:
Whence to Learn? Transferring Knowledge in Configurable Systems Using BEETLE. IEEE Trans. Software Eng. 47(12): 2956-2972 (2021) - [c163]Sarah Elder, Nusrat Zahan, Valeri Kozarev, Rui Shu, Tim Menzies, Laurie A. Williams:
Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard. ICSE (SEET) 2021: 95-104 - [c162]N. C. Shrikanth, Suvodeep Majumder, Tim Menzies:
Early Life Cycle Software Defect Prediction. Why? How? ICSE 2021: 448-459 - [c161]Tim Menzies, Kewen Peng, Andre Lustosa:
Fairer Software Made Easier (using "Keys"). ASE Workshops 2021: 108-113 - [c160]Huy Tu, Tim Menzies:
FRUGAL: Unlocking Semi-Supervised Learning for Software Analytics. ASE 2021: 394-406 - [c159]Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic:
Lessons learned from hyper-parameter tuning for microservice candidate identification. ASE 2021: 1141-1145 - [c158]Huy Tu, George Papadimitriou, Mariam Kiran, Cong Wang, Anirban Mandal, Ewa Deelman, Tim Menzies:
Mining Workflows for Anomalous Data Transfers. MSR 2021: 1-12 - [c157]Joymallya Chakraborty, Suvodeep Majumder, Tim Menzies:
Bias in machine learning software: why? how? what to do? ESEC/SIGSOFT FSE 2021: 429-440 - [c156]Rahul Yedida, Tim Menzies:
Documenting evidence of a reuse of 'a systematic study of the class imbalance problem in convolutional neural networks'. ESEC/SIGSOFT FSE 2021: 1595 - [c155]Rahul Yedida, Tim Menzies:
Documenting evidence of a reuse of 'on the number of linear regions of deep neural networks'. ESEC/SIGSOFT FSE 2021: 1596 - [c154]Andre Lustosa, Tim Menzies:
Documenting evidence of a reuse of 'a systematic literature review of techniques and metrics to reduce the cost of mutation testing'. ESEC/SIGSOFT FSE 2021: 1597 - [c153]Andre Lustosa, Tim Menzies:
Documenting evidence of a reuse of 'RefactoringMiner 2.0'. ESEC/SIGSOFT FSE 2021: 1598 - [c152]Kewen Peng, Tim Menzies:
Documenting evidence of a reuse of 'what is a feature? a qualitative study of features in industrial software product lines'. ESEC/SIGSOFT FSE 2021: 1599 - [c151]Kewen Peng, Tim Menzies:
Documenting evidence of a reuse of '"why should I trust you?": explaining the predictions of any classifier'. ESEC/SIGSOFT FSE 2021: 1600 - [c150]Xueqi Yang, Tim Menzies:
Documenting evidence of a replication of 'populating a release history database from version control and bug tracking systems'. ESEC/SIGSOFT FSE 2021: 1601 - [c149]Xueqi Yang, Tim Menzies:
Documenting evidence of a replication of 'analyze this! 145 questions for data scientists in software engineering'. ESEC/SIGSOFT FSE 2021: 1602 - [c148]Xueqi Yang, Tim Menzies:
Documenting evidence of a reproduction of 'is there a "golden" feature set for static warning identification? - an experimental evaluation'. ESEC/SIGSOFT FSE 2021: 1603 - [i99]Jianfeng Chen, Xipeng Shen, Tim Menzies:
Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization). CoRR abs/2101.02817 (2021) - [i98]Rahul Yedida, Xueqi Yang, Tim Menzies:
When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time. CoRR abs/2101.06319 (2021) - [i97]Sarah Elder, Nusrat Zahan, Val Kozarev, Rui Shu, Tim Menzies, Laurie A. Williams:
Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard. CoRR abs/2103.05088 (2021) - [i96]Huy Tu, George Papadimitriou, Mariam Kiran, Cong Wang, Anirban Mandal, Ewa Deelman, Tim Menzies:
Mining Scientific Workflows for Anomalous Data Transfers. CoRR abs/2103.12221 (2021) - [i95]N. C. Shrikanth, Tim Menzies:
The Early Bird Catches the Worm: Better Early Life Cycle Defect Predictors. CoRR abs/2105.11082 (2021) - [i94]Joymallya Chakraborty, Suvodeep Majumder, Tim Menzies:
Bias in Machine Learning Software: Why? How? What to do? CoRR abs/2105.12195 (2021) - [i93]Kewen Peng, Christian Kaltenecker, Norbert Siegmund, Sven Apel, Tim Menzies:
VEER: Disagreement-Free Multi-objective Configuration. CoRR abs/2106.02716 (2021) - [i92]Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic:
Lessons learned from hyper-parameter tuning for microservice candidate identification. CoRR abs/2106.06652 (2021) - [i91]Tim Menzies, Kewen Peng, Andre Lustosa:
Fairer Software Made Easier (using "Keys"). CoRR abs/2107.05088 (2021) - [i90]Maria Teresa Baldassarre, Neil A. Ernst, Ben Hermann, Tim Menzies, Rahul Yedida:
Crowdsourcing the State of the Art(ifacts). CoRR abs/2108.06821 (2021) - [i89]Huy Tu, Tim Menzies:
FRUGAL: Unlocking SSL for Software Analytics. CoRR abs/2108.09847 (2021) - [i88]Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic:
Partitioning Cloud-based Microservices (via Deep Learning). CoRR abs/2109.14569 (2021) - [i87]Kewen Peng, Joymallya Chakraborty, Tim Menzies:
xFAIR: Better Fairness via Model-based Rebalancing of Protected Attributes. CoRR abs/2110.01109 (2021) - [i86]Mehdi Bahrami, N. C. Shrikanth, Shade Ruangwan, Lei Liu, Yuji Mizobuchi, Masahiro Fukuyori, Wei-Peng Chen, Kazuki Munakata, Tim Menzies:
PyTorrent: A Python Library Corpus for Large-scale Language Models. CoRR abs/2110.01710 (2021) - [i85]Suvodeep Majumder, Joymallya Chakraborty, Gina R. Bai, Kathryn T. Stolee, Tim Menzies:
Fair Enough: Searching for Sufficient Measures of Fairness. CoRR abs/2110.13029 (2021) - [i84]Joymallya Chakraborty, Huy Tu, Suvodeep Majumder, Tim Menzies:
Can We Achieve Fairness Using Semi-Supervised Learning? CoRR abs/2111.02038 (2021) - [i83]Xiao Ling, Tim Menzies:
Faster Multi-Goal Simulation-Based Testing Using DoLesS (Domination with Least Square Approximation). CoRR abs/2112.01598 (2021) - 2020
- [j109]Amritanshu Agrawal, Tim Menzies, Leandro L. Minku, Markus Wagner, Zhe Yu:
Better software analytics via "DUO": Data mining algorithms using/used-by optimizers. Empir. Softw. Eng. 25(3): 2099-2136 (2020) - [j108]Rahul Krishna, Tim Menzies:
Learning actionable analytics from multiple software projects. Empir. Softw. Eng. 25(5): 3468-3500 (2020) - [j107]Tim Menzies:
The Five Laws of SE for AI. IEEE Softw. 37(1): 81-85 (2020) - [j106]Anita D. Carleton, Erin Harper, Tim Menzies, Tao Xie, Sigrid Eldh, Michael R. Lyu:
The AI Effect: Working at the Intersection of AI and SE. IEEE Softw. 37(4): 26-35 (2020) - [j105]Anita D. Carleton, Erin Harper, Michael R. Lyu, Sigrid Eldh, Tao Xie, Tim Menzies:
Expert Perspectives on AI. IEEE Softw. 37(4): 87-94 (2020) - [j104]Junjie Wang, Ye Yang, Tim Menzies, Qing Wang:
iSENSE2.0: Improving Completion-aware Crowdtesting Management with Duplicate Tagger and Sanity Checker. ACM Trans. Softw. Eng. Methodol. 29(4): 24:1-24:27 (2020) - [j103]Vivek Nair, Zhe Yu, Tim Menzies, Norbert Siegmund, Sven Apel:
Finding Faster Configurations Using FLASH. IEEE Trans. Software Eng. 46(7): 794-811 (2020) - [c147]N. C. Shrikanth, Tim Menzies:
Assessing practitioner beliefs about software defect prediction. ICSE (SEIP) 2020: 182-190 - [c146]N. C. Shrikanth, Tim Menzies:
What disconnects practitioner belief and empirical evidence? ICSE (Companion Volume) 2020: 286-287 - [c145]Joymallya Chakraborty, Kewen Peng, Tim Menzies:
Making Fair ML Software using Trustworthy Explanation. ASE 2020: 1229-1233 - [c144]Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu, Tim Menzies:
Fairway: a way to build fair ML software. ESEC/SIGSOFT FSE 2020: 654-665 - [e11]Leandro L. Minku, Tim Menzies, Meiyappan Nagappan:
PROMISE '20: 16th International Conference on Predictive Models and Data Analytics in Software Engineering, Virtual Event, USA, November 8-9, 2020. ACM 2020, ISBN 978-1-4503-8127-7 [contents] - [i82]Zhe Yu, Fahmid Morshed Fahid, Huy Tu, Tim Menzies:
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach. CoRR abs/2002.11049 (2020) - [i81]Huy Tu, Rishabh Agrawal, Tim Menzies:
The Changing Nature of Computational Science Software. CoRR abs/2003.05922 (2020) - [i80]Kewen Peng, Tim Menzies:
How to Improve AI Tools (by Adding in SE Knowledge): Experiments with the TimeLIME Defect Reduction Tool. CoRR abs/2003.06887 (2020) - [i79]Joymallya Chakraborty, Suvodeep Majumder, Zhe Wu, Tim Menzies:
Fairway: SE Principles for Building Fairer Software. CoRR abs/2003.10354 (2020) - [i78]Xueqi Yang, Jianfeng Chen, Rahul Yedida, Zhe Yu, Tim Menzies:
How to Recognize Actionable Static Code Warnings (Using Linear SVMs). CoRR abs/2006.00444 (2020) - [i77]N. C. Shrikanth, William Nichols, Fahmid Morshed Fahid, Tim Menzies:
Assessing Practitioner Beliefs about Software Engineering. CoRR abs/2006.05060 (2020) - [i76]Tianpei Xia, Wei Fu, Rui Shu, Tim Menzies:
Predicting Project Health for Open Source Projects (using the DECART Hyperparameter Optimizer). CoRR abs/2006.07240 (2020) - [i75]Kewen Peng, Tim Menzies:
Defect Reduction Planning (using TimeLIME). CoRR abs/2006.07416 (2020) - [i74]Joymallya Chakraborty, Kewen Peng, Tim Menzies:
Making Fair ML Software using Trustworthy Explanation. CoRR abs/2007.02893 (2020) - [i73]Xiao Ling, Rishabh Agrawal, Tim Menzies:
How Different is Test Case Prioritization for Open and Closed Source Projects? CoRR abs/2008.00612 (2020) - [i72]Rahul Yedida, Tim Menzies:
Improving Deep Learning for Defect Prediction (using the GHOST Hyperparameter Optimizer). CoRR abs/2008.03835 (2020) - [i71]Suvodeep Majumder, Pranav Mody, Tim Menzies:
Revisiting Process versus Product Metrics: a Large Scale Analysis. CoRR abs/2008.09569 (2020) - [i70]Rui Shu, Tianpei Xia, Laurie A. Williams, Tim Menzies:
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack. CoRR abs/2011.12720 (2020) - [i69]N. C. Shrikanth, Suvodeep Majumder, Tim Menzies:
Early Life Cycle Software Defect Prediction. Why? How? CoRR abs/2011.13071 (2020)
2010 – 2019
- 2019
- [j102]Zhe Yu, Tim Menzies:
FAST2: An intelligent assistant for finding relevant papers. Expert Syst. Appl. 120: 57-71 (2019) - [j101]Junjie Wang, Mingyang Li, Song Wang, Tim Menzies, Qing Wang:
Images don't lie: Duplicate crowdtesting reports detection with screenshot information. Inf. Softw. Technol. 110: 139-155 (2019) - [j100]Tim Menzies, Martin J. Shepperd:
"Bad smells" in software analytics papers. Inf. Softw. Technol. 112: 35-47 (2019) - [j99]Jianfeng Chen, Vivek Nair, Rahul Krishna, Tim Menzies:
"Sampling" as a Baseline Optimizer for Search-Based Software Engineering. IEEE Trans. Software Eng. 45(6): 597-614 (2019) - [j98]Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, Tim Menzies:
A Deep Learning Model for Estimating Story Points. IEEE Trans. Software Eng. 45(7): 637-656 (2019) - [j97]Rahul Krishna, Tim Menzies:
Bellwethers: A Baseline Method for Transfer Learning. IEEE Trans. Software Eng. 45(11): 1081-1105 (2019) - [c143]Tim Menzies:
Take control: on the unreasonable effectiveness of software analytics. ICSE (SEIP) 2019: 265-266 - [c142]Junjie Wang, Ye Yang, Rahul Krishna, Tim Menzies, Qing Wang:
iSENSE: completion-aware crowdtesting management. ICSE 2019: 912-923 - [c141]Di Chen, Kathryn T. Stolee, Tim Menzies:
Replication can improve prior results: a GitHub study of pull request acceptance. ICPC 2019: 179-190 - [c140]Zhe Yu, Fahmid M. Fahid, Tim Menzies, Gregg Rothermel, Kyle Patrick, Snehit Cherian:
TERMINATOR: better automated UI test case prioritization. ESEC/SIGSOFT FSE 2019: 883-894 - [c139]Jianfeng Chen, Joymallya Chakraborty, Philip Clark, Kevin Haverlock, Snehit Cherian, Tim Menzies:
Predicting breakdowns in cloud services (with SPIKE). ESEC/SIGSOFT FSE 2019: 916-924 - [e10]Tim Menzies, Burak Turhan:
Proceedings of the 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE@ICSE 2019, Montreal, QC, Canada, May 28, 2019. IEEE / ACM 2019, ISBN 978-1-7281-2272-4 [contents] - [i68]Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen, Tim Menzies:
How to "DODGE" Complex Software Analytics? CoRR abs/1902.01838 (2019) - [i67]Di Chen, Kathryn T. Stolee, Tim Menzies:
Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance. CoRR abs/1902.04060 (2019) - [i66]N. C. Shrikanth, Tim Menzies:
Assessing Developer Beliefs: A Reply to "Perceptions, Expectations, and Challenges in Defect Prediction". CoRR abs/1904.05794 (2019) - [i65]Suvodeep Majumder, Joymallya Chakraborty, Amritanshu Agrawal, Tim Menzies:
Why Software Projects need Heroes (Lessons Learned from 1100+ Projects). CoRR abs/1904.09954 (2019) - [i64]Huy Tu, Tim Menzies:
Enhanced Labeling of Issue Reports (with F3T). CoRR abs/1905.01719 (2019) - [i63]Jianfeng Chen, Tim Menzies:
Faster Creation of Smaller Test Suites (with SNAP). CoRR abs/1905.05358 (2019) - [i62]Joymallya Chakraborty, Tianpei Xia, Fahmid M. Fahid, Tim Menzies:
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization. CoRR abs/1905.05786 (2019) - [i61]Jianfeng Chen, Joymallya Chakraborty, Philip Clark, Kevin Haverlock, Snehit Cherian, Tim Menzies:
Predicting Breakdowns in Cloud Services (with SPIKE). CoRR abs/1905.06390 (2019) - [i60]Rui Shu, Tianpei Xia, Laurie A. Williams, Tim Menzies:
Better Security Bug Report Classification via Hyperparameter Optimization. CoRR abs/1905.06872 (2019) - [i59]Zhe Yu, Fahmid M. Fahid, Tim Menzies, Gregg Rothermel, Kyle Patrick, Snehit Cherian:
TERMINATOR: Better Automated UI Test Case Prioritization. CoRR abs/1905.07019 (2019) - [i58]Fahmid M. Fahid, Zhe Yu, Tim Menzies:
Better Technical Debt Detection via SURVEYing. CoRR abs/1905.08297 (2019) - [i57]Zhe Yu, Jeffrey C. Carver, Gregg Rothermel, Tim Menzies:
Searching for Better Test Case Prioritization Schemes: a Case Study of AI-assisted Systematic Literature Review. CoRR abs/1909.07249 (2019) - [i56]Xueqi Yang, Zhe Yu, Junjie Wang, Tim Menzies:
An Expert System for Learning Software Engineering Knowledge (with Case Studies in Understanding Static Code Warning). CoRR abs/1911.01387 (2019) - [i55]Rahul Krishna, Vivek Nair, Pooyan Jamshidi, Tim Menzies:
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE. CoRR abs/1911.01817 (2019) - [i54]Rui Shu, Tianpei Xia, Jianfeng Chen, Laurie A. Williams, Tim Menzies:
Improved Recognition of Security Bugs via Dual Hyperparameter Optimization. CoRR abs/1911.02476 (2019) - [i53]Suvodeep Majumder, Rahul Krishna, Tim Menzies:
Learning GENERAL Principles from Hundreds of Software Projects. CoRR abs/1911.04250 (2019) - [i52]Amritanshu Agrawal, Tim Menzies:
Is AI different for SE? CoRR abs/1912.04061 (2019) - [i51]Tianpei Xia, Jianfeng Chen, Rui Shu, Tim Menzies:
Sequential Model Optimization for Software Process Control. CoRR abs/1912.04189 (2019) - [i50]N. C. Shrikanth, Tim Menzies:
Assessing Practitioner Beliefs. CoRR abs/1912.10093 (2019) - 2018
- [j96]Vivek Nair, Tim Menzies, Norbert Siegmund, Sven Apel:
Faster discovery of faster system configurations with spectral learning. Autom. Softw. Eng. 25(2): 247-277 (2018) - [j95]Zhe Yu, Nicholas A. Kraft, Tim Menzies:
Finding better active learners for faster literature reviews. Empir. Softw. Eng. 23(6): 3161-3186 (2018) - [j94]Jianfeng Chen, Vivek Nair, Tim Menzies:
Beyond evolutionary algorithms for search-based software engineering. Inf. Softw. Technol. 95: 281-294 (2018) - [j93]Amritanshu Agrawal, Wei Fu, Tim Menzies:
What is wrong with topic modeling? And how to fix it using search-based software engineering. Inf. Softw. Technol. 98: 74-88 (2018) - [j92]Justyna Petke, Tim Menzies:
Guest Editorial for the Special Section from the 9th International Symposium on Search Based Software Engineering. Inf. Softw. Technol. 104: 194 (2018) - [j91]Rafael Prikladnicki, Tim Menzies:
From Voice of Evidence to Redirections. IEEE Softw. 35(1): 11-13 (2018) - [j90]Ye Yang, Davide Falessi, Tim Menzies, Jairus Hihn:
Actionable Analytics for Software Engineering. IEEE Softw. 35(1): 51-53 (2018) - [j89]Tim Menzies:
The Unreasonable Effectiveness of Software Analytics. IEEE Softw. 35(2): 96-98 (2018) - [j88]Tim Menzies, Thomas Zimmermann:
Software Analytics: What's Next? IEEE Softw. 35(5): 64-70 (2018) - [j87]George Mathew, Tim Menzies:
Software Engineering's Top Topics, Trends, and Researchers. IEEE Softw. 35(5): 88-93 (2018) - [j86]Jaechang Nam, Wei Fu, Sunghun Kim, Tim Menzies, Lin Tan:
Heterogeneous Defect Prediction. IEEE Trans. Software Eng. 44(9): 874-896 (2018) - [c138]Jianfeng Chen, Tim Menzies:
RIOT: A Stochastic-Based Method for Workflow Scheduling in the Cloud. IEEE CLOUD 2018: 318-325 - [c137]Chin-Jung Hsu, Vivek Nair, Tim Menzies, Vincent W. Freeh:
Micky: A Cheaper Alternative for Selecting Cloud Instances. IEEE CLOUD 2018: 409-416 - [c136]Tim Menzies:
Introduction to the Minitrack on Frontiers in AI and Software Engineering. HICSS 2018: 1 - [c135]Chin-Jung Hsu, Vivek Nair, Vincent W. Freeh, Tim Menzies:
Arrow: Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM. ICDCS 2018: 660-670 - [c134]Amritanshu Agrawal, Akond Rahman, Rahul Krishna, Alexander Sobran, Tim Menzies:
We don't need another hero?: the impact of "heroes" on software development. ICSE (SEIP) 2018: 245-253 - [c133]Rahul Krishna, Amritanshu Agrawal, Akond Rahman, Alexander Sobran, Tim Menzies:
What is the connection between issues, bugs, and enhancements?: lessons learned from 800+ software projects. ICSE (SEIP) 2018: 306-315 - [c132]Amritanshu Agrawal, Tim Menzies:
Is "better data" better than "better data miners"?: on the benefits of tuning SMOTE for defect prediction. ICSE 2018: 1050-1061 - [c131]Vivek Nair, Amritanshu Agrawal, Jianfeng Chen, Wei Fu, George Mathew, Tim Menzies, Leandro L. Minku, Markus Wagner, Zhe Yu:
Data-driven search-based software engineering. MSR 2018: 341-352 - [c130]Suvodeep Majumder, Nikhila Balaji, Katie Brey, Wei Fu, Tim Menzies:
500+ times faster than deep learning: a case study exploring faster methods for text mining stackoverflow. MSR 2018: 554-563 - [c129]Zhe Yu, Tim Menzies:
Total recall, language processing, and software engineering. NL4SE@ESEC/SIGSOFT FSE 2018: 10-13 - [c128]Di Chen, Wei Fu, Rahul Krishna, Tim Menzies:
Applications of psychological science for actionable analytics. ESEC/SIGSOFT FSE 2018: 456-467 - [e9]Olga Baysal, Tim Menzies:
Proceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics, SWAN@ESEC/SIGSOFT FSE 2018, Lake Buena Vista, FL, USA, November 5, 2018. ACM 2018, ISBN 978-1-4503-6056-2 [contents] - [i49]Vivek Nair, Zhe Yu, Tim Menzies, Norbert Siegmund, Sven Apel:
Finding Faster Configurations using FLASH. CoRR abs/1801.02175 (2018) - [i48]Vivek Nair, Amritanshu Agrawal, Jianfeng Chen, Wei Fu, George Mathew, Tim Menzies, Leandro L. Minku, Markus Wagner, Zhe Yu:
Data-Driven Search-based Software Engineering. CoRR abs/1801.10241 (2018) - [i47]Suvodeep Majumder, Nikhila Balaji, Katie Brey, Wei Fu, Tim Menzies:
500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow). CoRR abs/1802.05319 (2018) - [i46]Chin-Jung Hsu, Vivek Nair, Tim Menzies, Vincent W. Freeh:
Scout: An Experienced Guide to Find the Best Cloud Configuration. CoRR abs/1803.01296 (2018) - [i45]Vivek Nair, Rahul Krishna, Tim Menzies, Pooyan Jamshidi:
Transfer Learning with Bellwethers to find Good Configurations. CoRR abs/1803.03900 (2018) - [i44]Wei Fu, Tim Menzies, Di Chen, Amritanshu Agrawal:
Building Better Quality Predictors Using "ε-Dominance". CoRR abs/1803.04608 (2018) - [i43]Di Chen, Wei Fu, Rahul Krishna, Tim Menzies:
Applications of Psychological Science for Actionable Analytics. CoRR abs/1803.05067 (2018) - [i42]Rahul Krishna, Suvodeep Majumder, Tim Menzies, Martin J. Shepperd:
Bad Smells in Software Analytics Papers. CoRR abs/1803.05518 (2018) - [i41]Chin-Jung Hsu, Vivek Nair, Tim Menzies, Vincent W. Freeh:
Micky: A Cheaper Alternative for Selecting Cloud Instances. CoRR abs/1803.05587 (2018) - [i40]Zhe Yu, Christopher Theisen, Hyunwoo Sohn, Laurie A. Williams, Tim Menzies:
Cost-aware Vulnerability Prediction: the HARMLESS Approach. CoRR abs/1803.06545 (2018) - [i39]Tianpei Xia, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies:
Why Software Effort Estimation Needs SBSE. CoRR abs/1804.00626 (2018) - [i38]Amritanshu Agrawal, Huy Tu, Tim Menzies:
Can You Explain That, Better? Comprehensible Text Analytics for SE Applications. CoRR abs/1804.10657 (2018) - [i37]Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies:
Hyperparameter Optimization for Effort Estimation. CoRR abs/1805.00336 (2018) - [i36]Junjie Wang, Ye Yang, Rahul Krishna, Tim Menzies, Qing Wang:
Effective Automated Decision Support for Managing Crowdtesting. CoRR abs/1805.02744 (2018) - [i35]Junjie Wang, Mingyang Li, Song Wang, Tim Menzies, Qing Wang:
Cutting Away the Confusion From Crowdtesting. CoRR abs/1805.02763 (2018) - [i34]Junjie Wang, Ye Yang, Zhe Yu, Tim Menzies, Qing Wang:
Crowdtesting : When is The Party Over? CoRR abs/1805.03218 (2018) - [i33]George Mathew, Tim Menzies:
Better Metrics for Ranking SE Researchers. CoRR abs/1805.12124 (2018) - [i32]Zhe Yu, Tim Menzies:
Total Recall, Language Processing, and Software Engineering. CoRR abs/1809.00039 (2018) - [i31]Amritanshu Agrawal, Tim Menzies, Leandro L. Minku, Markus Wagner, Zhe Yu:
Better Software Analytics via "DUO": Data Mining Algorithms Using/Used-by Optimizers. CoRR abs/1812.01550 (2018) - 2017
- [j85]Marouane Kessentini, Tim Menzies:
A guest editorial: special issue on search based software engineering and data mining. Autom. Softw. Eng. 24(3): 573-574 (2017) - [j84]Tim Menzies, William Nichols, Forrest Shull, Lucas Layman:
Are delayed issues harder to resolve? Revisiting cost-to-fix of defects throughout the lifecycle. Empir. Softw. Eng. 22(4): 1903-1935 (2017) - [j83]Tim Menzies, Ye Yang, George Mathew, Barry W. Boehm, Jairus Hihn:
Negative results for software effort estimation. Empir. Softw. Eng. 22(5): 2658-2683 (2017) - [j82]Rahul Krishna, Tim Menzies, Lucas Layman:
Less is more: Minimizing code reorganization using XTREE. Inf. Softw. Technol. 88: 53-66 (2017) - [j81]Rahul Pandita, Raoul Jetley, Sithu D. Sudarsan, Tim Menzies, Laurie A. Williams:
TMAP: Discovering relevant API methods through text mining of API documentation. J. Softw. Evol. Process. 29(12) (2017) - [c127]Zhe Yu, Tim Menzies:
Data Balancing for Technologically Assisted Reviews: Undersampling or Reweighting. CLEF (Working Notes) 2017 - [c126]Neil A. Ernst, John Klein, George Mathew, Tim Menzies:
Using Stakeholder Preferences to Make Better Architecture Decisions. ICSA Workshops 2017: 133-136 - [c125]George Mathew, Amritanshu Agrawal, Tim Menzies:
Trends in topics at SE conferences (1993-2013). ICSE (Companion Volume) 2017: 397-398 - [c124]George Mathew, Tim Menzies, Neil A. Ernst, John Klein:
"SHORT"er Reasoning About Larger Requirements Models. RE 2017: 154-163 - [c123]Wei Fu, Tim Menzies:
Easy over hard: a case study on deep learning. ESEC/SIGSOFT FSE 2017: 49-60 - [c122]Wei Fu, Tim Menzies:
Revisiting unsupervised learning for defect prediction. ESEC/SIGSOFT FSE 2017: 72-83 - [c121]Vivek Nair, Tim Menzies, Norbert Siegmund, Sven Apel:
Using bad learners to find good configurations. ESEC/SIGSOFT FSE 2017: 257-267 - [e8]Olga Baysal, Tim Menzies:
Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics, SWAN@ESEC/SIGSOFT FSE 2017, Paderborn, Germany, September 4, 2017. ACM 2017, ISBN 978-1-4503-5157-7 [contents] - [e7]Tim Menzies, Justyna Petke:
Search Based Software Engineering - 9th International Symposium, SSBSE 2017, Paderborn, Germany, September 9-11, 2017, Proceedings. Lecture Notes in Computer Science 10452, Springer 2017, ISBN 978-3-319-66298-5 [contents] - [i30]Jianfeng Chen, Vivek Nair, Tim Menzies:
Beyond Evolutionary Algorithms for Search-based Software Engineering. CoRR abs/1701.07950 (2017) - [i29]Vivek Nair, Tim Menzies, Norbert Siegmund, Sven Apel:
Faster Discovery of Faster System Configurations with Spectral Learning. CoRR abs/1701.08106 (2017) - [i28]George Mathew, Tim Menzies, Neil A. Ernst, John Klein:
Shorter Reasoning About Larger Requirements Models. CoRR abs/1702.05568 (2017) - [i27]Vivek Nair, Tim Menzies, Norbert Siegmund, Sven Apel:
Using Bad Learners to find Good Configurations. CoRR abs/1702.05701 (2017) - [i26]Mitch Rees-Jones, Matthew Martin, Tim Menzies:
Better Predictors for Issue Lifetime. CoRR abs/1702.07735 (2017) - [i25]Di Chen, Kathryn T. Stolee, Tim Menzies:
Replicating and Scaling up Qualitative Analysis using Crowdsourcing: A Github-based Case Study. CoRR abs/1702.08571 (2017) - [i24]Wei Fu, Tim Menzies:
Revisiting Unsupervised Learning for Defect Prediction. CoRR abs/1703.00132 (2017) - [i23]Wei Fu, Tim Menzies:
Easy over Hard: A Case Study on Deep Learning. CoRR abs/1703.00133 (2017) - [i22]Rahul Krishna, Tim Menzies:
Simpler Transfer Learning (Using "Bellwethers"). CoRR abs/1703.06218 (2017) - [i21]Amritanshu Agrawal, Tim Menzies:
"Better Data" is Better than "Better Data Miners" (Benefits of Tuning SMOTE for Defect Prediction). CoRR abs/1705.03697 (2017) - [i20]Vivek Nair, Zhe Yu, Tim Menzies:
FLASH: A Faster Optimizer for SBSE Tasks. CoRR abs/1705.05018 (2017) - [i19]Zhe Yu, Tim Menzies:
Testing Reading Tactics for Automated Reading Assistance: Is it Useful to Apply Old Knowledge? CoRR abs/1705.05420 (2017) - [i18]Rahul Krishna, Tim Menzies:
Learning Effective Changes For Software Projects. CoRR abs/1708.05442 (2017) - [i17]Jianfeng Chen, Tim Menzies:
RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows. CoRR abs/1708.08127 (2017) - [i16]Rahul Krishna, Amritanshu Agrawal, Akond Rahman, Alexander Sobran, Tim Menzies:
What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects). CoRR abs/1710.08736 (2017) - [i15]Amritanshu Agrawal, Akond Rahman, Rahul Krishna, Alexander Sobran, Tim Menzies:
We Don't Need Another Hero? The Impact of "Heroes" on Software Development. CoRR abs/1710.09055 (2017) - [i14]Akond Rahman, Amritanshu Agrawal, Rahul Krishna, Alexander Sobran, Tim Menzies:
Continuous Integration: The Silver Bullet? CoRR abs/1711.03933 (2017) - [i13]Chin-Jung Hsu, Vivek Nair, Vincent W. Freeh, Tim Menzies:
Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM. CoRR abs/1712.10081 (2017) - 2016
- [j80]Wei Fu, Tim Menzies, Xipeng Shen:
Tuning for software analytics: Is it really necessary? Inf. Softw. Technol. 76: 135-146 (2016) - [j79]Joseph Krall, Tim Menzies, Misty D. Davies:
Learning Mitigations for Pilot Issues When Landing Aircraft (via Multiobjective Optimization and Multiagent Simulations). IEEE Trans. Hum. Mach. Syst. 46(2): 221-230 (2016) - [c120]Tim Menzies:
How not to do it: anti-patterns for data science in software engineering. ICSE (Companion Volume) 2016: 887 - [c119]Rahul Krishna, Tim Menzies, Wei Fu:
Too much automation? the bellwether effect and its implications for transfer learning. ASE 2016: 122-131 - [c118]Lucas Layman, Allen P. Nikora, Joshua Meek, Tim Menzies:
Topic modeling of NASA space system problem reports: research in practice. MSR 2016: 303-314 - [c117]Vivek Nair, Tim Menzies, Jianfeng Chen:
An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE. SSBSE 2016: 96-111 - [p6]Tim Menzies:
Seven principles of inductive software engineering. Perspectives on Data Science for Software Engineering 2016: 13-17 - [p5]Tim Menzies:
Correlation is not causation (or, when not to scream "Eureka!"). Perspectives on Data Science for Software Engineering 2016: 327-330 - [e6]Tim Menzies, Laurie A. Williams, Thomas Zimmermann:
Perspectives on Data Science for Software Engineering. Academic Press 2016, ISBN 978-0-12-804206-9 [contents] - [i12]Jianfeng Chen, Vivek Nair, Rahul Krishna, Tim Menzies:
Is "Sampling" better than "Evolution" for Search-based Software Engineering? CoRR abs/1608.07617 (2016) - [i11]George Mathew, Amritanshu Agrawal, Tim Menzies:
Trends in Topics at SE Conferences (1993-2013). CoRR abs/1608.08100 (2016) - [i10]Amritanshu Agrawal, Wei Fu, Tim Menzies:
What is Wrong with Topic Modeling? (and How to Fix it Using Search-based SE). CoRR abs/1608.08176 (2016) - [i9]Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, Tim Menzies:
A deep learning model for estimating story points. CoRR abs/1609.00489 (2016) - [i8]Wei Fu, Tim Menzies, Xipeng Shen:
Tuning for Software Analytics: is it Really Necessary? CoRR abs/1609.01759 (2016) - [i7]Wei Fu, Vivek Nair, Tim Menzies:
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors? CoRR abs/1609.02613 (2016) - [i6]Rahul Krishna, Tim Menzies, Lucas Layman:
Recommendations for Intelligent Code Reorganization. CoRR abs/1609.03614 (2016) - [i5]Tim Menzies, William Nichols, Forrest Shull, Lucas Layman:
Are Delayed Issues Harder to Resolve? Revisiting Cost-to-Fix of Defects throughout the Lifecycle. CoRR abs/1609.04886 (2016) - [i4]Tim Menzies, Ye Yang, George Mathew, Barry W. Boehm, Jairus Hihn:
Negative Results for Software Effort Estimation. CoRR abs/1609.05563 (2016) - [i3]Zhe Yu, Nicholas A. Kraft, Tim Menzies:
How to Read Less: Better Machine Assisted Reading Methods for Systematic Literature Reviews. CoRR abs/1612.03224 (2016) - [i2]George Mathew, Tim Menzies, Jairus Hihn:
Impacts of Bad ESP (Early Size Predictions) on Software Effort Estimation. CoRR abs/1612.03240 (2016) - 2015
- [j78]Rachel Harrison, Tim Menzies:
Guest editorial: special issue on realizing AI synergies in software engineering. Autom. Softw. Eng. 22(1): 1-2 (2015) - [j77]Rachel Harrison, Tim Menzies:
Guest editorial: special issue on realizing AI synergies in software engineering (part 2). Autom. Softw. Eng. 22(2): 143-144 (2015) - [j76]Tim Menzies, Corina S. Pasareanu:
Guest editorial: special multi-issue on selected topics in Automated Software Engineering. Autom. Softw. Eng. 22(3): 289-290 (2015) - [j75]Tim Menzies, Corina S. Pasareanu:
Guest editorial: special multi-issue on selected topics in automated software engineering. Autom. Softw. Eng. 22(4): 437-438 (2015) - [j74]Tim Menzies:
Cross-Project Data for Software Engineering. Computer 48(12): 6 (2015) - [j73]Ekrem Kocaguneli, Tim Menzies, Emilia Mendes:
Transfer learning in effort estimation. Empir. Softw. Eng. 20(3): 813-843 (2015) - [j72]Joseph Krall, Tim Menzies, Misty D. Davies:
GALE: Geometric Active Learning for Search-Based Software Engineering. IEEE Trans. Software Eng. 41(10): 1001-1018 (2015) - [c116]Fayola Peters, Tim Menzies, Lucas Layman:
LACE2: Better Privacy-Preserving Data Sharing for Cross Project Defect Prediction. ICSE (1) 2015: 801-811 - [c115]Tim Menzies, Leandro L. Minku, Fayola Peters:
The Art and Science of Analyzing Software Data; Quantitative Methods. ICSE (2) 2015: 959-960 - [c114]Luciano Baresi, Tim Menzies, Andreas Metzger, Thomas Zimmermann:
1st International Workshop on Big Data Software Engineering (BIGDSE 2015). ICSE (2) 2015: 965-966 - [c113]Jairus Hihn, Tim Menzies:
Data Mining Methods and Cost Estimation Models: Why is it So Hard to Infuse New Ideas? ASE Workshops 2015: 5-9 - [c112]Rahul Krishna, Tim Menzies:
Actionable = Cluster + Contrast? ASE Workshops 2015: 14-17 - [p4]Christian Bird, Tim Menzies, Thomas Zimmermann:
Past, Present, and Future of Analyzing Software Data. The Art and Science of Analyzing Software Data 2015: 1-13 - [e5]Christian Bird, Tim Menzies, Thomas Zimmermann:
The Art and Science of Analyzing Software Data. Morgan Kaufmann / Elsevier 2015, ISBN 978-0-12-411519-4 [contents] - [e4]Luciano Baresi, Tim Menzies, Andreas Metzger, Thomas Zimmermann:
1st IEEE/ACM International Workshop on Big Data Software Engineering, BIGDSE 2015, Florence, Italy, May 23, 2015. IEEE Computer Society 2015, ISBN 978-1-4673-7025-7 [contents] - 2014
- [j71]Tim Menzies, Marjan Mernik:
Special issue on realizing artificial intelligence synergies in software engineering. Softw. Qual. J. 22(1): 49-50 (2014) - [c111]Joseph Krall, Tim Menzies, Misty D. Davies:
Learning the Task Management Space of an Aircraft Approach Model. AAAI Spring Symposia 2014 - [c110]Tim Menzies, Christian Bird, Thomas Zimmermann:
Analyzing software data: after the gold rush (a goldfish-bowl panel). ICSE Companion 2014: 103-104 - [p3]Tim Menzies:
Occam's Razor and Simple Software Project Management. Software Project Management in a Changing World 2014: 447-472 - [p2]Tim Menzies:
Data Mining. Recommendation Systems in Software Engineering 2014: 39-75 - [i1]Harald C. Gall, Tim Menzies, Laurie A. Williams, Thomas Zimmermann:
Software Development Analytics (Dagstuhl Seminar 14261). Dagstuhl Reports 4(6): 64-83 (2014) - 2013
- [j70]Jacky Keung, Ekrem Kocaguneli, Tim Menzies:
Finding conclusion stability for selecting the best effort predictor in software effort estimation. Autom. Softw. Eng. 20(4): 543-567 (2013) - [j69]Ekrem Kocaguneli, Tim Menzies, Jacky W. Keung:
Kernel methods for software effort estimation - Effects of different kernel functions and bandwidths on estimation accuracy. Empir. Softw. Eng. 18(1): 1-24 (2013) - [j68]Tim Menzies, Günes Koru:
Predictive models in software engineering. Empir. Softw. Eng. 18(3): 433-434 (2013) - [j67]Yue Jiang, Bojan Cukic, Tim Menzies, Jie Lin:
Incremental Development of Fault Prediction Models. Int. J. Softw. Eng. Knowl. Eng. 23(10): 1399-1426 (2013) - [j66]Tim Menzies:
Guest editorial for the Special Section on BEST PAPERS from the 2011 conference on Predictive Models in Software Engineering (PROMISE). Inf. Softw. Technol. 55(8): 1477-1478 (2013) - [j65]Ekrem Kocaguneli, Tim Menzies:
Software effort models should be assessed via leave-one-out validation. J. Syst. Softw. 86(7): 1879-1890 (2013) - [j64]Tim Menzies:
Beyond Data Mining. IEEE Softw. 30(3): 92 (2013) - [j63]Tim Menzies, Thomas Zimmermann:
Software Analytics: So What? IEEE Softw. 30(4): 31-37 (2013) - [j62]Tim Menzies, Thomas Zimmermann:
The Many Faces of Software Analytics. IEEE Softw. 30(5): 28-29 (2013) - [j61]Tim Menzies, Andrew Butcher, David R. Cok, Andrian Marcus, Lucas Layman, Forrest Shull, Burak Turhan, Thomas Zimmermann:
Local versus Global Lessons for Defect Prediction and Effort Estimation. IEEE Trans. Software Eng. 39(6): 822-834 (2013) - [j60]Ekrem Kocaguneli, Tim Menzies, Jacky Keung, David R. Cok, Raymond J. Madachy:
Active Learning and Effort Estimation: Finding the Essential Content of Software Effort Estimation Data. IEEE Trans. Software Eng. 39(8): 1040-1053 (2013) - [j59]Fayola Peters, Tim Menzies, Liang Gong, Hongyu Zhang:
Balancing Privacy and Utility in Cross-Company Defect Prediction. IEEE Trans. Software Eng. 39(8): 1054-1068 (2013) - [j58]Tim Menzies, Adam Brady, Jacky Keung, Jairus Hihn, Steve Williams, Oussama El-Rawas, Phillip Green II, Barry W. Boehm:
Learning Project Management Decisions: A Case Study with Case-Based Reasoning versus Data Farming. IEEE Trans. Software Eng. 39(12): 1698-1713 (2013) - [c109]Zhimin He, Fayola Peters, Tim Menzies, Ye Yang:
Learning from Open-Source Projects: An Empirical Study on Defect Prediction. ESEM 2013: 45-54 - [c108]Abdel Salam Sayyad, Joseph Ingram, Tim Menzies, Hany H. Ammar:
Optimum feature selection in software product lines: Let your model and values guide your search. CMSBSE@ICSE 2013: 22-27 - [c107]Abdel Salam Sayyad, Tim Menzies, Hany H. Ammar:
On the value of user preferences in search-based software engineering: a case study in software product lines. ICSE 2013: 492-501 - [c106]Sonia Haiduc, Gabriele Bavota, Andrian Marcus, Rocco Oliveto, Andrea De Lucia, Tim Menzies:
Automatic query reformulations for text retrieval in software engineering. ICSE 2013: 842-851 - [c105]Ekrem Kocaguneli, Thomas Zimmermann, Christian Bird, Nachiappan Nagappan, Tim Menzies:
Distributed development considered harmful? ICSE 2013: 882-890 - [c104]Tim Menzies, Ekrem Kocaguneli, Fayola Peters, Burak Turhan, Leandro L. Minku:
Data science for software engineering. ICSE 2013: 1484-1486 - [c103]Christian Bird, Tim Menzies, Thomas Zimmermann:
1st international workshop on data analysis patterns in software engineering (DAPSE 2013). ICSE 2013: 1517-1518 - [c102]Rachel Harrison, Sol J. Greenspan, Tim Menzies, Marjan Mernik, Pedro Rangel Henriques, Daniela Carneiro da Cruz, Daniel Rodríguez:
2nd international workshop on realizing artificial intelligence synergies in software engineering (RAISE 2013). ICSE 2013: 1543-1544 - [c101]Christian Bird, Tim Menzies, Thomas Zimmermann:
Foreword. DAPSE@ICSE 2013: iii-iv - [c100]Abdel Salam Sayyad, Joseph Ingram, Tim Menzies, Hany H. Ammar:
Scalable product line configuration: A straw to break the camel's back. ASE 2013: 465-474 - [c99]Giuseppe Scanniello, Carmine Gravino, Andrian Marcus, Tim Menzies:
Class level fault prediction using software clustering. ASE 2013: 640-645 - [c98]Fayola Peters, Tim Menzies, Andrian Marcus:
Better cross company defect prediction. MSR 2013: 409-418 - [c97]Tim Menzies:
Beyond data mining; towards "idea engineering". PROMISE 2013: 11:1-11:6 - [c96]Ekrem Kocaguneli, Bojan Cukic, Tim Menzies, Huihua Lu:
Building a second opinion: learning cross-company data. PROMISE 2013: 12:1-12:10 - 2012
- [j57]Ayse Basar Bener, Tim Menzies:
Guest editorial: learning to organize testing. Autom. Softw. Eng. 19(2): 137-140 (2012) - [j56]Tim Menzies, Martin J. Shepperd:
Special issue on repeatable results in software engineering prediction. Empir. Softw. Eng. 17(1-2): 1-17 (2012) - [j55]Markus Lumpe, Rajesh Vasa, Tim Menzies, Rebecca Rush, Burak Turhan:
Learning Better Inspection Optimization Policies. Int. J. Softw. Eng. Knowl. Eng. 22(5): 621-644 (2012) - [j54]Rachel Harrison, Daniela Carneiro da Cruz, Pedro Rangel Henriques, Maria João Varanda Pereira, Shih-Hsi Liu, Tim Menzies, Marjan Mernik, Daniel Rodríguez:
Report from the first international workshop on realizing artificial intelligence synergies in software engineering (RAISE 2012). ACM SIGSOFT Softw. Eng. Notes 37(5): 34-35 (2012) - [j53]Ekrem Kocaguneli, Tim Menzies, Ayse Bener, Jacky W. Keung:
Exploiting the Essential Assumptions of Analogy-Based Effort Estimation. IEEE Trans. Software Eng. 38(2): 425-438 (2012) - [j52]Ekrem Kocaguneli, Tim Menzies, Jacky W. Keung:
On the Value of Ensemble Effort Estimation. IEEE Trans. Software Eng. 38(6): 1403-1416 (2012) - [c95]Abdel Salam Sayyad, Hany H. Ammar, Tim Menzies:
Software feature model recommendations using data mining. RSSE@ICSE 2012: 47-51 - [c94]Fayola Peters, Tim Menzies:
Privacy and utility for defect prediction: Experiments with MORPH. ICSE 2012: 189-199 - [c93]Tim Menzies, Thomas Zimmermann:
Goldfish bowl panel: Software development analytics. ICSE 2012: 1032-1033 - [c92]Yang Sok Kim, Byeong Ho Kang, Seung Hwan Ryu, Paul Compton, Soyeon Caren Han, Tim Menzies:
Crowd-Sourced Knowledge Bases. PKAW 2012: 258-271 - [c91]Raymond Borges, Tim Menzies:
Learning to change projects. PROMISE 2012: 11-18 - [c90]Ekrem Kocaguneli, Tim Menzies, Jairus Hihn, Byeong Ho Kang:
Size doesn't matter?: on the value of software size features for effort estimation. PROMISE 2012: 89-98 - [e3]Michael Goedicke, Tim Menzies, Motoshi Saeki:
IEEE/ACM International Conference on Automated Software Engineering, ASE'12, Essen, Germany, September 3-7, 2012. ACM 2012, ISBN 978-1-4503-1204-2 [contents] - 2011
- [j51]Ashutosh Nandeshwar, Tim Menzies, Adam Nelson:
Learning patterns of university student retention. Expert Syst. Appl. 38(12): 14984-14996 (2011) - [j50]Topi Haapio, Tim Menzies:
Exploring the Effort of General Software Project Activities with Data Mining. Int. J. Softw. Eng. Knowl. Eng. 21(5): 725-753 (2011) - [j49]Adam Nelson, Tim Menzies, Gregory Gay:
Sharing experiments using open-source software. Softw. Pract. Exp. 41(3): 283-305 (2011) - [j48]James H. Andrews, Tim Menzies, Felix Chun Hang Li:
Genetic Algorithms for Randomized Unit Testing. IEEE Trans. Software Eng. 37(1): 80-94 (2011) - [c89]Ekrem Kocaguneli, Tim Menzies:
How to Find Relevant Data for Effort Estimation? ESEM 2011: 255-264 - [c88]Tim Menzies, Andrew Butcher, Andrian Marcus, Thomas Zimmermann, David R. Cok:
Local vs. global models for effort estimation and defect prediction. ASE 2011: 343-351 - [p1]Tim Menzies, Forrest Shull:
The Quest for Convincing Evidence. Making Software 2011: 3-16 - [e2]Tim Menzies:
Proceedings of the 7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Banff, Alberta, Canada, September 20-21, 2011. ACM 2011, ISBN 978-1-4503-0709-3 [contents] - 2010
- [j47]Gregory Gay, Tim Menzies, Omid Jalali, Gregory E. Mundy, Beau Gilkerson, Martin S. Feather, James D. Kiper:
Finding robust solutions in requirements models. Autom. Softw. Eng. 17(1): 87-116 (2010) - [j46]Tim Menzies, Zach Milton, Burak Turhan, Bojan Cukic, Yue Jiang, Ayse Basar Bener:
Defect prediction from static code features: current results, limitations, new approaches. Autom. Softw. Eng. 17(4): 375-407 (2010) - [j45]Tim Menzies, Omid Jalali, Jairus Hihn, Daniel Baker, Karen T. Lum:
Stable rankings for different effort models. Autom. Softw. Eng. 17(4): 409-437 (2010) - [j44]Gregory Gay, Tim Menzies, Misty D. Davies, Karen Gundy-Burlet:
Automatically finding the control variables for complex system behavior. Autom. Softw. Eng. 17(4): 439-468 (2010) - [j43]Ayse Tosun, Ayse Basar Bener, Burak Turhan, Tim Menzies:
Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry. Inf. Softw. Technol. 52(11): 1242-1257 (2010) - [j42]Oussama El-Rawas, Tim Menzies:
A second look at Faster, Better, Cheaper. Innov. Syst. Softw. Eng. 6(4): 319-335 (2010) - [j41]Adam Brady, Tim Menzies, Oussama El-Rawas, Ekrem Kocaguneli, Jacky W. Keung:
Case-Based Reasoning for Reducing Software Development Effort. J. Softw. Eng. Appl. 3(11): 1005-1014 (2010) - [c87]LiGuo Huang, Daniel Port, Liang Wang, Tao Xie, Tim Menzies:
Text mining in supporting software systems risk assurance. ASE 2010: 163-166 - [c86]Ekrem Kocaguneli, Gregory Gay, Tim Menzies, Ye Yang, Jacky W. Keung:
When to use data from other projects for effort estimation. ASE 2010: 321-324 - [c85]Burak Turhan, Ayse Basar Bener, Tim Menzies:
Regularities in Learning Defect Predictors. PROFES 2010: 116-130 - [c84]Adam Brady, Tim Menzies:
Case-based reasoning vs parametric models for software quality optimization. PROMISE 2010: 3 - [c83]Hongyu Zhang, Adam Nelson, Tim Menzies:
On the value of learning from defect dense components for software defect prediction. PROMISE 2010: 14 - [c82]Earl T. Barr, Christian Bird, Eric Hyatt, Tim Menzies, Gregorio Robles:
On the shoulders of giants. FoSER 2010: 23-28 - [c81]Andrian Marcus, Tim Menzies:
Software is data too. FoSER 2010: 229-232 - [e1]Tim Menzies, Günes Koru:
Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE 2010, Timisoara, Romania, September 12-13, 2010. ACM 2010, ISBN 978-1-4503-0404-7 [contents]
2000 – 2009
- 2009
- [j40]Letha H. Etzkorn, Tim Menzies:
Special issue on information retrieval for program comprehension. Empir. Softw. Eng. 14(1): 1-4 (2009) - [j39]Burak Turhan, Tim Menzies, Ayse Basar Bener, Justin S. Di Stefano:
On the relative value of cross-company and within-company data for defect prediction. Empir. Softw. Eng. 14(5): 540-578 (2009) - [j38]Tim Menzies, Osamu Mizuno, Yasunari Takagi, Tohru Kikuno:
Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects. J. Softw. Eng. Appl. 2(4): 221-236 (2009) - [j37]Tim Menzies, Steve Williams, Oussama El-Rawas, Daniel Baker, Barry W. Boehm, Jairus Hihn, Karen T. Lum, Raymond J. Madachy:
Accurate estimates without local data? Softw. Process. Improv. Pract. 14(4): 213-225 (2009) - [c80]Topi Haapio, Tim Menzies:
Data mining with software industry project data: A case study. IADIS AC (2) 2009: 33-38 - [c79]Tim Menzies, Steve Williams, Barry W. Boehm, Jairus Hihn:
How to avoid drastic software process change (using stochastic stability). ICSE 2009: 540-550 - [c78]Gregory Gay, Sonia Haiduc, Andrian Marcus, Tim Menzies:
On the use of relevance feedback in IR-based concept location. ICSM 2009: 351-360 - [c77]Andres S. Orrego, Tim Menzies, Oussama El-Rawas:
On the Relative Merits of Software Reuse. ICSP 2009: 186-197 - [c76]Yue Jiang, Jie Lin, Bojan Cukic, Tim Menzies:
Variance Analysis in Software Fault Prediction Models. ISSRE 2009: 99-108 - [c75]Joseph D'Alessandro, Cynthia D. Tanner, Bonnie W. Morris, Tim Menzies:
Is Continuous Compliance Assurance Possible? ITNG 2009: 1599 - [c74]Phillip Green II, Tim Menzies, Steve Williams, Oussama El-Rawas:
Understanding the Value of Software Engineering Technologies. ASE 2009: 52-61 - [c73]Bryan Lemon, Aaron Riesbeck, Tim Menzies, Justin Price, Joseph D'Alessandro, Rikard Carlsson, Tomi Prifiti, Fayola Peters, Huihua Lu, Daniel Port:
Applications of Simulation and AI Search: Assessing the Relative Merits of Agile vs Traditional Software Development. ASE 2009: 580-584 - [c72]Tim Menzies, Oussama El-Rawas, Jairus Hihn, Barry W. Boehm:
Can we build software faster and better and cheaper? PROMISE 2009: 2 - [c71]James H. Andrews, Tim Menzies:
On the value of combining feature subset selection with genetic algorithms: faster learning of coverage models. PROMISE 2009: 13 - [c70]Gregory Gay, Tim Menzies, Bojan Cukic, Burak Turhan:
How to build repeatable experiments. PROMISE 2009: 15 - 2008
- [j36]Tim Menzies:
Editorial, special issue, repeatable experiments in software engineering. Empir. Softw. Eng. 13(5): 469-471 (2008) - [j35]Tim Menzies, Markland Benson, Ken Costello, Christina Moats, Melissa Northey, Julian Richardson:
Learning better IV&V practices. Innov. Syst. Softw. Eng. 4(2): 169-183 (2008) - [j34]Martin S. Feather, Steven L. Cornford, Kenneth A. Hicks, James D. Kiper, Tim Menzies:
A Broad, Quantitative Model for Making Early Requirements Decisions. IEEE Softw. 25(2): 49-56 (2008) - [c69]Gary D. Boetticher, Tim Menzies, Thomas J. Ostrand, Günther Ruhe:
4th international workshop on predictor models in SE (PROMISE 2008). ICSE Companion 2008: 1061-1062 - [c68]Tim Menzies, Andrian Marcus:
Automated severity assessment of software defect reports. ICSM 2008: 346-355 - [c67]Tim Menzies, Oussama El-Rawas, Barry W. Boehm, Raymond J. Madachy, Jairus Hihn, Daniel Baker, Karen T. Lum:
Accurate Estimates without Calibration? ICSP 2008: 210-221 - [c66]Yue Jiang, Bojan Cukic, Tim Menzies:
Cost Curve Evaluation of Fault Prediction Models. ISSRE 2008: 197-206 - [c65]Yue Jiang, Bojan Cukic, Tim Menzies:
Can data transformation help in the detection of fault-prone modules? DEFECTS 2008: 16-20 - [c64]Burak Turhan, Ayse Basar Bener, Tim Menzies:
Nearest neighbor sampling for cross company defect predictors: abstract only. DEFECTS 2008: 26 - [c63]Daniel Port, Alexy Olkov, Tim Menzies:
Using Simulation to Investigate Requirements Prioritization Strategies. ASE 2008: 268-277 - [c62]Johann Schumann, Karen Gundy-Burlet, Corina S. Pasareanu, Tim Menzies, Tony Barrett:
Tool Support for Parametric Analysis of Large Software Simulation Systems. ASE 2008: 497-498 - [c61]Yue Jiang, Bojan Cukic, Tim Menzies, Nick Bartlow:
Comparing design and code metrics for software quality prediction. PROMISE@ICSE 2008: 11-18 - [c60]Tim Menzies, Burak Turhan, Ayse Bener, Gregory Gay, Bojan Cukic, Yue Jiang:
Implications of ceiling effects in defect predictors. PROMISE@ICSE 2008: 47-54 - [c59]Omid Jalali, Tim Menzies, Martin S. Feather:
Optimizing requirements decisions with keys. PROMISE@ICSE 2008: 79-86 - 2007
- [j33]Tim Menzies, David Owen, Julian Richardson:
The Strangest Thing About Software. Computer 40(1): 54-60 (2007) - [j32]Tim Menzies, Jeremy Greenwald, Art Frank:
Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Trans. Software Eng. 33(1): 2-13 (2007) - [j31]Tim Menzies, Alex Dekhtyar, Justin S. Di Stefano, Jeremy Greenwald:
Problems with Precision: A Response to "Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors'". IEEE Trans. Software Eng. 33(9): 637-640 (2007) - [c58]Yue Jiang, Bojan Cukic, Tim Menzies:
Fault Prediction using Early Lifecycle Data. ISSRE 2007: 237-246 - [c57]James H. Andrews, Felix Chun Hang Li, Tim Menzies:
Nighthawk: a two-level genetic-random unit test data generator. ASE 2007: 144-153 - [c56]Tim Menzies, Oussama El-Rawas, Jairus Hihn, Martin S. Feather, Raymond J. Madachy, Barry W. Boehm:
The business case for automated software engineering. ASE 2007: 303-312 - [c55]Omid Jalali, Tim Menzies, Daniel Baker, Jairus Hihn:
Column Pruning Beats Stratification in Effort Estimation. PROMISE@ICSE 2007: 7 - 2006
- [j30]Tim Menzies, Ying Hu:
Just enough learning (of association rules): the TAR2 "Treatment" learner. Artif. Intell. Rev. 25(3): 211-229 (2006) - [j29]Tim Menzies, Julian Richardson:
Making Sense of Requirements, Sooner. Computer 39(10): 112-114 (2006) - [j28]Tim Menzies, Jairus Hihn:
Evidence-Based Cost Estimation for Better-Quality Software. IEEE Softw. 23(4): 64-66 (2006) - [j27]Tim Menzies, Zhihao Chen, Jairus Hihn, Karen T. Lum:
Selecting Best Practices for Effort Estimation. IEEE Trans. Software Eng. 32(11): 883-895 (2006) - [c54]Jimin Gao, Mats Per Erik Heimdahl, David Owen, Tim Menzies:
On the Distribution of Property Violations in Formal Models: An Initial Study. COMPSAC (1) 2006: 150-160 - [c53]Marcus S. Fisher, Tim Menzies:
Learning IV&V Strategies. HICSS 2006 - [c52]Tim Menzies, Julian Richardson:
Qualitative Modeling for Requirements Engineering. SEW 2006: 11-20 - 2005
- [j26]Tim Menzies, Charles Pecheur:
Verification and Validation and Artificial Intelligence. Adv. Comput. 65: 154-203 (2005) - [j25]Zhihao Chen, Tim Menzies, Daniel Port, Barry W. Boehm:
Feature subset selection can improve software cost estimation accuracy. ACM SIGSOFT Softw. Eng. Notes 30(4): 1-6 (2005) - [j24]Tim Menzies, Daniel Port, Zhihao Chen, Jairus Hihn:
Simple software cost analysis: safe or unsafe? ACM SIGSOFT Softw. Eng. Notes 30(4): 1-6 (2005) - [j23]Zhihao Chen, Barry W. Boehm, Tim Menzies, Daniel Port:
Finding the Right Data for Software Cost Modeling. IEEE Softw. 22(6): 38-46 (2005) - [c51]Tim Menzies, Daniel Port, Zhihao Chen, Jairus Hihn, Sherry Stukes:
Validation methods for calibrating software effort models. ICSE 2005: 587-595 - [c50]Jelber Sayyad-Shirabad, Tim Menzies:
Predictor models in software engineering (PROMISE). ICSE 2005: 692 - [c49]Tim Menzies, Daniel Port, Zhihao Chen, Jairus Hihn:
Specialization and extrapolation of software cost models. ASE 2005: 384-387 - [c48]Tim Menzies, Dan Port, Zhihao Chen, Jairus Hihn:
Simple software cost analysis: safe or unsafe? PROMISE@ICSE 2005: 6:1-6:6 - [c47]Zhihao Chen, Tim Menzies, Dan Port, Barry W. Boehm:
Feature subset selection can improve software cost estimation accuracy. PROMISE@ICSE 2005: 8:1-8:6 - 2004
- [c46]Tim Menzies, Justin S. Di Stefano:
How Good Is Your Blind Spot Sampling Policy? HASE 2004: 129-138 - [c45]Alexander Dekhtyar, Jane Huffman Hayes, Tim Menzies:
Text is Software Too. MSR 2004: 22-26 - [c44]Tim Menzies, Justin S. Di Stefano, Chris Cunanan, Robert (Mike) Chapman:
Mining Repositories to Assist in Project Planning and Resource Allocation. MSR 2004: 75-79 - 2003
- [j22]Tim Menzies, Ying Hu:
Data Mining for Very Busy People. Computer 36(11): 22-29 (2003) - [j21]Tim Menzies:
Guest Editor's Introduction: 21st Century AI--Proud, Not Smug. IEEE Intell. Syst. 18(3): 18-24 (2003) - [j20]Tim Menzies:
Editorial: model-based requirements engineering. Requir. Eng. 8(4): 193-194 (2003) - [j19]Tim Menzies, Justin S. Di Stefano:
More Success and Failure Factors in Software Reuse. IEEE Trans. Software Eng. 29(5): 474-477 (2003) - [c43]Martin S. Feather, Tim Menzies, Judith R. Connelly:
Matching Software Practitioner Needs to Researcher Activities. APSEC 2003: 6-16 - [c42]Yan Liu, Srikanth Gururajan, Bojan Cukic, Tim Menzies, Marcello R. Napolitano:
Validating an Online Adaptive System Using SVDD. ICTAI 2003: 384- - [c41]Tim Menzies, Justin S. Di Stefano, Mike Chapman:
Learning Early Lifecycle IV&V Quality Indicators. IEEE METRICS 2003: 88-97 - [c40]Tim Menzies, Justin S. Di Stefano, Kareem Ammar, Kenneth McGill, Pat Callis, Robert (Mike) Chapman, John Davis:
When Can We Test Less? IEEE METRICS 2003: 98- - [c39]Martin S. Feather, Tim Menzies, Judith R. Connelly:
Relating Practitioner Needs to Research Activities. RE 2003: 352- - [c38]David Owen, Tim Menzies:
Lurch: a Lightweight Alternative to Model Checking. SEKE 2003: 158-165 - [c37]Tim Menzies, Robyn R. Lutz, Ines Carmen Mikulski:
Better Analysis of Defect Data at NASA. SEKE 2003: 607-611 - [c36]Tim Menzies, James D. Kiper, Martin S. Feather:
Improved Software Engineering Decision Support Through Automatic Argument Reduction Tools. SEKE 2003: 655-662 - [c35]David Owen, Tim Menzies, Mats Per Erik Heimdahl, Jimin Gao:
On the Advantages of Approximate vs. Complete Verification: Bigger Models, Faster, Less Memory, Usually Accurate. SEW 2003: 75 - [c34]Dustin Geletko, Tim Menzies:
Model-Based Software Testing via Incremental Treatment Learning. SEW 2003: 82 - 2002
- [j18]Eliza Chiang, Tim Menzies:
Simulations for very early lifecycle quality evaluations. Softw. Process. Improv. Pract. 7(3-4): 141-159 (2002) - [j17]Tim Menzies, Robert F. Cohen, Sam Waugh, Simon Goss:
Applications of Abduction: Testing Very Long Qualitative Simulations. IEEE Trans. Knowl. Data Eng. 14(6): 1362-1375 (2002) - [c33]Tim Menzies, Adrian R. Pearce, Clinton Heinze, Simon Goss:
What Is an Agent and Why Should I Care? FAABS 2002: 1-14 - [c32]Tim Menzies, David Owen, Bojan Cukic:
You Seem Friendly, But Can I Trust You? FAABS 2002: 208-219 - [c31]David Owen, Bojan Cukic, Tim Menzies:
An Alternative to Model Checking: Verification by Random Search of AND-OR Graphs Representing Finite-State Models. HASE 2002: 119-128 - [c30]Yan Liu, Tim Menzies, Bojan Cukic:
Data Sniffing - Monitoring of Machine Learning for Online Adaptive Systems. ICTAI 2002: 16-21 - [c29]Justin S. Di Stefano, Tim Menzies:
Machine Learning for Software Engineering: Case Studies in Software Reuse. ICTAI 2002: 246-251 - [c28]Tim Menzies, David Owen, Bojan Cukic:
Saturation Effects in Testing of Formal Models. ISSRE 2002: 15-26 - [c27]Tim Menzies, David Raffo, Siri-on Setamanit, Ying Hu, Sina Tootoonia:
Model-Based Tests of Truisms. ASE 2002: 183- - [c26]David Owen, Tim Menzies, Bojan Cukic:
What Makes Finite-State Models More (or Less) Testable? ASE 2002: 237-240 - [c25]Martin S. Feather, Tim Menzies:
Converging on the Optimal Attainment of Requirements. RE 2002: 263-272 - [c24]Tim Menzies, Lindsay Mason:
Some prolog macros for rule-based programming: why? how? ACM SIGPLAN Workshop on Rule-Based Programming 2002: 79-92 - 2001
- [c23]Tim Menzies, Harshinder Singh:
How AI Can Help SE; or: Randomized Search Not Considered Harmful. AI 2001: 100-110 - [c22]Tim Menzies, John D. Powell, Michael E. Houle:
Fast Formal Analysis of Requirements via "Topoi Diagrams". ICSE 2001: 391-400 - [c21]Tim Menzies, James D. Kiper:
Better Reasoning About Software Engineering Activities. ASE 2001: 391-394 - 2000
- [j16]Brian Drabble, Laurent Chaudron, Catherine Tessier, Sue Abu-Hakima, Steven Willmott, Jim Austin, Boi Faltings, Eugene C. Freuder, Gerhard Friedrich, Alex Alves Freitas, Ulises Cortés, Miquel Sànchez-Marrè, David W. Aha, Irma Becerra-Fernandez, Héctor Muñoz-Avila, Aditya Ghose, Tim Menzies, Ken Satoh, Mary Elaine Califf, Michael T. Cox, Sandip Sen, Patrick Brézillon, Jean-Charles Pomerol, Roy M. Turner, Elise H. Turner:
Reports on the AAAI 1999 Workshop Program. AI Mag. 21(1): 95-100 (2000) - [j15]Tim Menzies, Bojan Cukic:
Adequacy of Limited Testing for Knowledge Based Systems. Int. J. Artif. Intell. Tools 9(1): 153-172 (2000) - [j14]Tim Menzies, Klaus-Dieter Althoff, Yannis Kalfoglou, Enrico Motta:
Issues with Meta-Knowledge. Int. J. Softw. Eng. Knowl. Eng. 10(4): 549-555 (2000) - [j13]Yannis Kalfoglou, Tim Menzies, Klaus-Dieter Althoff, Enrico Motta:
Meta-knowledge in systems design: panacea ... or undelivered promise? Knowl. Eng. Rev. 15(4): 381-404 (2000) - [j12]Tim Menzies, Bojan Cukic:
When to Test Less. IEEE Softw. 17(5): 107-112 (2000) - [c20]Tim Menzies, Bojan Cukic, Harshinder Singh:
Agents Talking Faster. FAABS 2000: 194-208 - [c19]Tim Menzies:
WISE3: the Third International Workshop on Intelligent Software Engineering (workshop session). ICSE 2000: 812-813 - [c18]Tim Menzies, Bojan Cukic, Harshinder Singh, John D. Powell:
Testing Nondeterminate Systems. ISSRE 2000: 222-233 - [c17]Tim Menzies:
The Complexity of TRMCS-like Spiral Specification. IWSSD 2000: 183-190 - [c16]Tim Menzies, Erik Sinsel:
Practical Large Scale What-If Queries: Case Studies with Software Risk Assessment. ASE 2000: 165-
1990 – 1999
- 1999
- [j11]Tim Menzies:
Desert Island Column. Autom. Softw. Eng. 6(3): 315-320 (1999) - [j10]Tim Menzies, Frank van Harmelen:
Editorial: Evaluating knowledge engineering techniques. Int. J. Hum. Comput. Stud. 51(4): 715-727 (1999) - [j9]Tim Menzies:
Critical success metrics: evaluation at the business level. Int. J. Hum. Comput. Stud. 51(4): 783-799 (1999) - [j8]Tim Menzies:
Knowledge maintenance: the state of the art. Knowl. Eng. Rev. 14(1): 1-46 (1999) - [j7]Tim Menzies:
Cost benefits of ontologies. Intell. 10(3): 26-32 (1999) - [c15]Tim Menzies, Bojan Cukic:
On the Sufficiency of Limited Testing for Knowledge Based Systems. ICTAI 1999: 431-440 - [c14]Tim Menzies, Steve M. Easterbrook, Bashar Nuseibeh, Sam Waugh:
An Empirical Investigation of Multiple Viewpoint Reasoning in Requirements Engineering. RE 1999: 100- - 1998
- [j6]Tim Menzies, William J. Clancey:
Editorial: the challenge of situated cognition for symbolic knowledge-based systems. Int. J. Hum. Comput. Stud. 49(6): 767-769 (1998) - [j5]Tim Menzies:
Towards situated knowledge acquisition. Int. J. Hum. Comput. Stud. 49(6): 867-893 (1998) - [c13]Tim Menzies, Sam Waugh:
Lower Bounds on the Size of Test Data Sets. Australian Joint Conference on Artificial Intelligence 1998: 227-237 - [c12]Sam Waugh, Brian Hanlon, Tim Menzies:
The Temporal QCM Modelling Language. Australian Joint Conference on Artificial Intelligence 1998: 262-272 - [c11]Tim Menzies, Sam Waugh:
Lower Bounds on the Size of Test Data Sets. EUROVAV 1998 - [c10]Tim Menzies, Sam Waugh:
On the Practicality of Viewpoint-Based Requirements Engineering. PRICAI 1998: 110-121 - 1997
- [j4]Tim Menzies, Paul Compton:
Applications of abduction: hypothesis testing of neuroendocrinological qualitative compartmental models. Artif. Intell. Medicine 10(2): 145-175 (1997) - [j3]Tim Menzies:
Object-Oriented Patterns: Lessons from Expert Systems. Softw. Pract. Exp. 27(12): 1457-1478 (1997) - [c9]Sam Waugh, Tim Menzies, Simon Goss:
Evaluating a Qualitative Reasoner. Australian Joint Conference on Artificial Intelligence 1997: 505-514 - [c8]Tim Menzies, Robert F. Cohen:
A Graph Theoretic Optimisation of Temporal Abductive Validation. EUROVAV 1997: 55-68 - 1996
- [j2]Tim Menzies:
Applications of abduction: knowledge-level modelling. Int. J. Hum. Comput. Stud. 45(3): 305-335 (1996) - [c7]Tim Menzies:
On the Practicality of Abductive Validation. ECAI 1996: 23-27 - [c6]Tim Menzies:
Visual Programming, Knowledge Engineering, and Software Engineering. SEKE 1996: 506-513 - 1993
- [c5]Philip Haynes, Tim Menzies:
"C++ is Better Than Smalltalk"?? TOOLS (12/9) 1993: 75-82 - [c4]Tim Menzies, Richard Spurrett:
How to Edit "It"; or: A "Black-box" Constraint-Based Framework for User-Interaction with Arbitrary Structures. TOOLS (12/9) 1993: 213-224 - [c3]Tim Menzies, Julian M. Edwards, Kekwee Ng:
The Mysterious Case of the Missing Reusable Class Libraries. TOOLS (12/9) 1993: 421-427 - 1992
- [c2]Tim Menzies:
Is-a Object Part-of Knowledge Representation (Part 2). TOOLS (6) 1992: 213-223
1980 – 1989
- 1989
- [j1]Tim Menzies:
An Investigation of AI and Expert Systems Literature: 1980-1984. AI Mag. 10(2): 53-61 (1989) - 1988
- [c1]Tim Menzies, M. Dean, J. L. Black, J. F. Fleming:
Combining Heuristics and Simulation Models: An Expert System for the Optimal Management of Pigs. Australian Joint Conference on Artificial Intelligence 1988: 48-61
Coauthor Index
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