


default search action
Hridesh Rajan
Person information
- affiliation: Iowa State University, Ames, Iowa, USA
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [c106]Shibbir Ahmed
, Hongyang Gao
, Hridesh Rajan
:
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment. ICSE 2024: 38:1-38:13 - [c105]David O'Brien
, Robert Dyer
, Tien N. Nguyen
, Hridesh Rajan
:
Data-Driven Evidence-Based Syntactic Sugar Design. ICSE 2024: 203:1-203:12 - [c104]David O'Brien
, Sumon Biswas
, Sayem Mohammad Imtiaz
, Rabe Abdalkareem
, Emad Shihab
, Hridesh Rajan
:
Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot. ICSE 2024: 219:1-219:13 - [i30]Shibbir Ahmed, Hongyang Gao, Hridesh Rajan:
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment. CoRR abs/2401.14628 (2024) - [i29]David O'Brien, Robert Dyer, Tien N. Nguyen, Hridesh Rajan:
Data-Driven Evidence-Based Syntactic Sugar Design. CoRR abs/2402.01079 (2024) - [i28]Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova, Mohammad Wardat, Gunel Jahangirova, Hridesh Rajan, Paolo Tonella:
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults. CoRR abs/2408.15150 (2024) - [i27]Ruchira Manke, Mohammad Wardat, Foutse Khomh, Hridesh Rajan:
Leveraging Data Characteristics for Bug Localization in Deep Learning Programs. CoRR abs/2412.05775 (2024) - 2023
- [j19]Syeda Khairunnesa Samantha, Shibbir Ahmed, Sayem Mohammad Imtiaz, Hridesh Rajan, Gary T. Leavens:
What kinds of contracts do ML APIs need? Empir. Softw. Eng. 28(6): 142 (2023) - [c103]Sayem Mohammad Imtiaz, Fraol Batole, Astha Singh, Rangeet Pan, Breno Dantas Cruz, Hridesh Rajan:
Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement. ICSE 2023: 1020-1032 - [c102]Usman Gohar
, Sumon Biswas, Hridesh Rajan:
Towards Understanding Fairness and its Composition in Ensemble Machine Learning. ICSE 2023: 1533-1545 - [c101]Sumon Biswas, Hridesh Rajan:
Fairify: Fairness Verification of Neural Networks. ICSE 2023: 1546-1558 - [c100]Ali Ghanbari
, Deepak-George Thomas, Muhammad Arbab Arshad, Hridesh Rajan:
Mutation-based Fault Localization of Deep Neural Networks. ASE 2023: 1301-1313 - [c99]Shibbir Ahmed
, Sayem Mohammad Imtiaz
, Syeda Khairunnesa Samantha
, Breno Dantas Cruz
, Hridesh Rajan
:
Design by Contract for Deep Learning APIs. ESEC/SIGSOFT FSE 2023: 94-106 - [c98]Giang Nguyen
, Sumon Biswas
, Hridesh Rajan
:
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML. ESEC/SIGSOFT FSE 2023: 502-514 - [i26]Shibbir Ahmed, Mohammad Wardat, Hamid Bagheri, Breno Dantas Cruz, Hridesh Rajan:
Characterizing Bugs in Python and R Data Analytics Programs. CoRR abs/2306.08632 (2023) - [i25]Giang Nguyen, Sumon Biswas, Hridesh Rajan:
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML. CoRR abs/2306.09297 (2023) - [i24]Mohammad Wardat, Breno Dantas Cruz, Wei Le, Hridesh Rajan:
An Effective Data-Driven Approach for Localizing Deep Learning Faults. CoRR abs/2307.08947 (2023) - [i23]Syeda Khairunnesa Samantha, Shibbir Ahmed, Sayem Mohammad Imtiaz, Hridesh Rajan, Gary T. Leavens:
What Kinds of Contracts Do ML APIs Need? CoRR abs/2307.14465 (2023) - [i22]Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad, Hridesh Rajan:
Mutation-based Fault Localization of Deep Neural Networks. CoRR abs/2309.05067 (2023) - 2022
- [c97]Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao:
A global convergence theory for deep ReLU implicit networks via over-parameterization. ICLR 2022 - [c96]Rangeet Pan, Hridesh Rajan:
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules. ICSE 2022: 524-535 - [c95]Mohammad Wardat
, Breno Dantas Cruz, Wei Le, Hridesh Rajan:
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs. ICSE 2022: 561-572 - [c94]Giang Nguyen, Md Johirul Islam, Rangeet Pan, Hridesh Rajan:
Manas: Mining Software Repositories to Assist AutoML. ICSE 2022: 1368-1380 - [c93]Sumon Biswas
, Mohammad Wardat
, Hridesh Rajan:
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. ICSE 2022: 2091-2103 - [c92]Menglu Yu, Ye Tian, Bo Ji
, Chuan Wu, Hridesh Rajan, Jia Liu:
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs. INFOCOM 2022: 1569-1578 - [c91]Hoan Anh Nguyen, Hung Dang Phan, Syeda Khairunnesa Samantha, Son Nguyen, Aashish Yadavally, Shaohua Wang, Hridesh Rajan, Tien N. Nguyen:
A Hybrid Approach for Inference between Behavioral Exception API Documentation and Implementations, and Its Applications. ASE 2022: 2:1-2:13 - [c90]Menglu Yu, Bo Ji
, Hridesh Rajan, Jia Liu:
On scheduling ring-all-reduce learning jobs in multi-tenant GPU clusters with communication contention. MobiHoc 2022: 21-30 - [c89]David O'Brien, Sumon Biswas, Sayem Imtiaz, Rabe Abdalkareem, Emad Shihab, Hridesh Rajan:
23 shades of self-admitted technical debt: an empirical study on machine learning software. ESEC/SIGSOFT FSE 2022: 734-746 - [i21]Menglu Yu, Ye Tian, Bo Ji, Chuan Wu, Hridesh Rajan, Jia Liu:
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs. CoRR abs/2202.01158 (2022) - [i20]Menglu Yu, Bo Ji, Hridesh Rajan, Jia Liu:
On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention. CoRR abs/2207.07817 (2022) - [i19]Rangeet Pan, Sumon Biswas, Mohna Chakraborty, Breno Dantas Cruz, Hridesh Rajan:
An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models. CoRR abs/2212.00105 (2022) - [i18]Usman Gohar, Sumon Biswas, Hridesh Rajan:
Towards Understanding Fairness and its Composition in Ensemble Machine Learning. CoRR abs/2212.04593 (2022) - [i17]Sayem Mohammad Imtiaz, Fraol Batole, Astha Singh, Rangeet Pan, Breno Dantas Cruz, Hridesh Rajan:
Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement. CoRR abs/2212.05970 (2022) - [i16]Sumon Biswas, Hridesh Rajan:
Fairify: Fairness Verification of Neural Networks. CoRR abs/2212.06140 (2022) - 2021
- [j18]Shibbir Ahmed
, Md Johirul Islam
, Hridesh Rajan
:
Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data. Trans. Data Sci. 2(4): 41:1-41:25 (2021) - [c88]Mohammad Wardat
, Wei Le, Hridesh Rajan:
DeepLocalize: Fault Localization for Deep Neural Networks. ICSE 2021: 251-262 - [c87]Sumon Biswas
, Hridesh Rajan:
Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline. ESEC/SIGSOFT FSE 2021: 981-993 - [e11]Hridesh Rajan:
SPLASH '21: Software for Humanity, Chicago, IL, USA, October 17 - 22, 2021, Companion Volume. ACM 2021, ISBN 978-1-4503-9088-0 [contents] - [i15]Mohammad Wardat, Wei Le, Hridesh Rajan:
DeepLocalize: Fault Localization for Deep Neural Networks. CoRR abs/2103.03376 (2021) - [i14]Sumon Biswas, Hridesh Rajan:
Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline. CoRR abs/2106.06054 (2021) - [i13]Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao:
A global convergence theory for deep ReLU implicit networks via over-parameterization. CoRR abs/2110.05645 (2021) - [i12]Rangeet Pan, Hridesh Rajan:
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules. CoRR abs/2110.07720 (2021) - [i11]Sumon Biswas
, Mohammad Wardat, Hridesh Rajan:
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. CoRR abs/2112.01590 (2021) - [i10]Giang Nguyen, Johir Islam, Rangeet Pan, Hridesh Rajan:
Manas: Mining Software Repositories to Assist AutoML. CoRR abs/2112.03395 (2021) - [i9]Mohammad Wardat, Breno Dantas Cruz, Wei Le, Hridesh Rajan:
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs. CoRR abs/2112.04036 (2021) - 2020
- [j17]Hamid Bagheri
, Andrew J. Severin, Hridesh Rajan
:
Detecting and correcting misclassified sequences in the large-scale public databases. Bioinform. 36(18): 4699-4705 (2020) - [c86]Ramanathan Ramu, Ganesha B. Upadhyaya, Hoan Anh Nguyen, Hridesh Rajan
:
BCFA: bespoke control flow analysis for CFA at scale. ICSE 2020: 1037-1048 - [c85]Md Johirul Islam, Rangeet Pan, Giang Nguyen, Hridesh Rajan
:
Repairing deep neural networks: fix patterns and challenges. ICSE 2020: 1135-1146 - [c84]Sumon Biswas
, Hridesh Rajan
:
Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. ESEC/SIGSOFT FSE 2020: 642-653 - [c83]Rangeet Pan
, Hridesh Rajan
:
On decomposing a deep neural network into modules. ESEC/SIGSOFT FSE 2020: 889-900 - [e10]Hridesh Rajan:
SPLASH '20: Conference on Systems, Programming, Languages, and Applications, Software for Humanity, Virtual Event, USA, November, 2020, Companion Volume. ACM 2020, ISBN 978-1-4503-8179-6 [contents] - [i8]Md Johirul Islam, Rangeet Pan, Giang Nguyen, Hridesh Rajan:
Repairing Deep Neural Networks: Fix Patterns and Challenges. CoRR abs/2005.00972 (2020) - [i7]Ramanathan Ramu, Ganesha B. Upadhyaya, Hoan Anh Nguyen, Hridesh Rajan:
BCFA: Bespoke Control Flow Analysis for CFA at Scale. CoRR abs/2005.01000 (2020) - [i6]Sumon Biswas, Hridesh Rajan:
Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness. CoRR abs/2005.12379 (2020)
2010 – 2019
- 2019
- [j16]Hamid Bagheri
, Usha Muppirala, Rick E. Masonbrink, Andrew J. Severin, Hridesh Rajan
:
Shared data science infrastructure for genomics data. BMC Bioinform. 20(1): 436:1-436:13 (2019) - [c82]Sumon Biswas
, Md Johirul Islam, Yijia Huang, Hridesh Rajan
:
Boa meets python: a boa dataset of data science software in python language. MSR 2019: 577-581 - [c81]Md Johirul Islam, Giang Nguyen, Rangeet Pan
, Hridesh Rajan
:
A comprehensive study on deep learning bug characteristics. ESEC/SIGSOFT FSE 2019: 510-520 - [i5]John L. Singleton, Gary T. Leavens, Hridesh Rajan, David R. Cok:
Inferring Concise Specifications of APIs. CoRR abs/1905.06847 (2019) - [i4]Rangeet Pan
, Md Johirul Islam, Shibbir Ahmed, Hridesh Rajan:
Identifying Classes Susceptible to Adversarial Attacks. CoRR abs/1905.13284 (2019) - [i3]Md Johirul Islam, Giang Nguyen, Rangeet Pan, Hridesh Rajan:
A Comprehensive Study on Deep Learning Bug Characteristics. CoRR abs/1906.01388 (2019) - [i2]Md Johirul Islam, Hoan Anh Nguyen, Rangeet Pan
, Hridesh Rajan:
What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow. CoRR abs/1906.11940 (2019) - 2018
- [j15]W. K. Chan
, Xiaodong Liu, Hridesh Rajan
:
Special issue on software engineering technology and applications. J. Syst. Softw. 137: 34-35 (2018) - [j14]Ganesha Upadhyaya
, Hridesh Rajan
:
On Accelerating Source Code Analysis at Massive Scale. IEEE Trans. Software Eng. 44(7): 669-688 (2018) - [c80]John L. Singleton, Gary T. Leavens, Hridesh Rajan
, David R. Cok:
An algorithm and tool to infer practical postconditions. ICSE (Companion Volume) 2018: 313-314 - [c79]Ramanathan Ramu, Ganesha Upadhyaya
, Hoan Anh Nguyen, Hridesh Rajan
:
Hybrid traversal: efficient source code analysis at scale. ICSE (Companion Volume) 2018: 412-413 - [c78]Ganesha Upadhyaya
, Hridesh Rajan:
Collective program analysis. ICSE 2018: 620-631 - [c77]Tianyi Zhang, Ganesha Upadhyaya
, Anastasia Reinhardt
, Hridesh Rajan
, Miryung Kim:
Are code examples on an online Q&A forum reliable?: a study of API misuse on stack overflow. ICSE 2018: 886-896 - [c76]Syeda Khairunnesa Samantha, Hoan Anh Nguyen, Hridesh Rajan
:
On the significance of contract-based typestate specification. WASPI@ESEC/SIGSOFT FSE 2018: 13-14 - [c75]Hoan Anh Nguyen, Tien N. Nguyen, Hridesh Rajan
, Robert Dyer:
Towards combining usage mining and implementation analysis to infer API preconditions. WASPI@ESEC/SIGSOFT FSE 2018: 15-16 - [c74]Jackson Maddox, Yuheng Long, Hridesh Rajan
:
Large-scale study of substitutability in the presence of effects. ESEC/SIGSOFT FSE 2018: 528-538 - [e9]Robert Dyer, Vasant G. Honavar, Gary T. Leavens, Hoan Anh Nguyen, Tien N. Nguyen, Hridesh Rajan:
Proceedings of the 1st ACM SIGSOFT International Workshop on Automated Specification Inference, WASPI@ESEC/SIGSOFT FSE, Lake Buena Vista, FL, USA, November 9, 2018. ACM 2018, ISBN 978-1-4503-6057-9 [contents] - [i1]Md Johirul Islam, Anuj Sharma, Hridesh Rajan:
A Cyberinfrastructure for BigData Transportation Engineering. CoRR abs/1805.00105 (2018) - 2017
- [j13]Syeda Khairunnesa Samantha, Hoan Anh Nguyen, Tien N. Nguyen, Hridesh Rajan:
Exploiting implicit beliefs to resolve sparse usage problem in usage-based specification mining. Proc. ACM Program. Lang. 1(OOPSLA): 83:1-83:29 (2017) - [c73]Mehdi Bagherzadeh, Hridesh Rajan
:
Order types: static reasoning about message races in asynchronous message passing concurrency. AGERE!@SPLASH 2017: 21-30 - [c72]Hridesh Rajan
, Alexander Bolotov, Chang Xu:
Message from SETA 2017 Program Chairs. COMPSAC (1) 2017: 1 - [c71]Hung Phan, Hoan Anh Nguyen, Tien N. Nguyen, Hridesh Rajan
:
Statistical Learning for Inference between Implementations and Documentation. ICSE-NIER 2017: 27-30 - [c70]Ganesha Upadhyaya
, Hridesh Rajan
:
On Accelerating Ultra-Large-Scale Mining. ICSE-NIER 2017: 39-42 - [c69]Nitin M. Tiwari, Ganesha Upadhyaya
, Hoan Anh Nguyen, Hridesh Rajan
:
Candoia: a platform for building and sharing mining software repositories tools as apps. MSR 2017: 53-63 - [e8]Stephen M. Blackburn, Christoph Bockisch, Michael Haupt, Tony Hosking
, Hridesh Rajan, Witawas Srisa-an, Matthias Grimmer, Adam Welc:
Proceedings of the 9th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages, Vancouver, BC, Canada, October 23 - 27, 2017. ACM 2017, ISBN 978-1-4503-5519-3 [contents] - 2016
- [j12]Mehdi Bagherzadeh, Robert Dyer, Rex D. Fernando, José Sánchez, Hridesh Rajan
:
Modular Reasoning in the Presence of Event Subtyping. LNCS Trans. Modul. Compos. 1: 167-223 (2016) - [c68]Yuheng Long, Hridesh Rajan
:
A type-and-effect system for asynchronous, typed events. MODULARITY 2016: 42-53 - [c67]Yuheng Long, Mehdi Bagherzadeh, Eric Lin, Ganesha Upadhyaya
, Hridesh Rajan
:
On ordering problems in message passing software. MODULARITY 2016: 54-65 - [c66]Doris L. Carver, Wing Kwong Chan
, Hongji Yang, Xiaodong Liu, Carl K. Chang, Hridesh Rajan
:
Message from the SETA Organizing Committee. COMPSAC 2016: 1 - [c65]Nitin M. Tiwari, Ganesha Upadhyaya
, Hridesh Rajan
:
Candoia: a platform and ecosystem for mining software repositories tools. ICSE (Companion Volume) 2016: 759-764 - [c64]Gary T. Leavens, David A. Naumann
, Hridesh Rajan
, Tomoyuki Aotani
:
Specifying and Verifying Advanced Control Features. ISoLA (2) 2016: 80-96 - [c63]Yuheng Long, Yu David Liu, Hridesh Rajan
:
First-class effect reflection for effect-guided programming. OOPSLA 2016: 820-837 - 2015
- [j11]Robert Dyer
, Hoan Anh Nguyen, Hridesh Rajan
, Tien N. Nguyen:
Boa: Ultra-Large-Scale Software Repository and Source-Code Mining. ACM Trans. Softw. Eng. Methodol. 25(1): 7:1-7:34 (2015) - [c62]Mehdi Bagherzadeh, Hridesh Rajan
:
Panini: a concurrent programming model for solving pervasive and oblivious interference. MODULARITY 2015: 93-108 - [c61]Mehdi Bagherzadeh, Robert Dyer
, Rex D. Fernando, José Sánchez, Hridesh Rajan
:
Modular reasoning in the presence of event subtyping. MODULARITY 2015: 117-132 - [c60]Yuheng Long, Yu David Liu, Hridesh Rajan
:
Intensional Effect Polymorphism. ECOOP 2015: 346-370 - [c59]Hridesh Rajan
, Tien N. Nguyen, Gary T. Leavens, Robert Dyer
:
Inferring Behavioral Specifications from Large-scale Repositories by Leveraging Collective Intelligence. ICSE (2) 2015: 579-582 - [c58]Hridesh Rajan
:
Capsule-Oriented Programming. ICSE (2) 2015: 611-614 - [c57]Hoan Anh Nguyen, Robert Dyer, Tien N. Nguyen, Hridesh Rajan
:
Consensus-based mining of API preconditions in big code. SPLASH (Companion Volume) 2015: 5-6 - [c56]Robert Dyer, Hridesh Rajan
, Tien N. Nguyen, Hoan Anh Nguyen:
Demonstrating programming language feature mining using Boa. SPLASH (Companion Volume) 2015: 13-14 - [c55]Ganesha Upadhyaya
, Hridesh Rajan
:
Effectively mapping linguistic abstractions for message-passing concurrency to threads on the Java virtual machine. OOPSLA 2015: 840-859 - [p1]Robert Dyer, Hoan Nguyen, Hridesh Rajan
, Tien N. Nguyen:
Boa. The Art and Science of Analyzing Software Data 2015: 593-621 - 2014
- [c54]Ganesha Upadhyaya
, Hridesh Rajan
:
An Automatic Actors to Threads Mapping Technique for JVM-Based Actor Frameworks. AGERE!@SPLASH 2014: 29-41 - [c53]Henrique Rebêlo, Gary T. Leavens, Mehdi Bagherzadeh, Hridesh Rajan
, Ricardo Massa Ferreira Lima, Daniel M. Zimmerman, Márcio Cornélio, Thomas Thüm:
Modularizing crosscutting contracts with AspectJML. MODULARITY (Companion) 2014: 21-24 - [c52]Henrique Rebêlo, Gary T. Leavens, Mehdi Bagherzadeh, Hridesh Rajan
, Ricardo Massa Ferreira Lima, Daniel M. Zimmerman, Márcio Cornélio, Thomas Thüm:
AspectJML: modular specification and runtime checking for crosscutting contracts. MODULARITY 2014: 157-168 - [c51]Robert Dyer
, Hridesh Rajan
, Hoan Anh Nguyen, Tien N. Nguyen:
Mining billions of AST nodes to study actual and potential usage of Java language features. ICSE 2014: 779-790 - [c50]Hoan Anh Nguyen, Robert Dyer
, Tien N. Nguyen, Hridesh Rajan
:
Mining preconditions of APIs in large-scale code corpus. SIGSOFT FSE 2014: 166-177 - [e7]Gary T. Leavens, Hidehiko Masuhara, Hridesh Rajan, Eric Bodden:
Proceedings of the 13th Workshop on Foundations of Aspect-Oriented Languages, FOAL 2014, April 22, 2014, Lugano, Switzerland. ACM 2014, ISBN 978-1-4503-2798-5 [contents] - 2013
- [j10]Robert Dyer, Hridesh Rajan
, Yuanfang Cai:
Language Features for Software Evolution and Aspect-Oriented Interfaces: An Exploratory Study. LNCS Trans. Aspect Oriented Softw. Dev. 10: 148-183 (2013) - [c49]Mehdi Bagherzadeh, Hridesh Rajan
, Mohammad Ali Darvish Darab
:
On exceptions, events and observer chains. AOSD 2013: 185-196 - [c48]Robert Dyer
, Hridesh Rajan
, Tien N. Nguyen:
Declarative visitors to ease fine-grained source code mining with full history on billions of AST nodes. GPCE 2013: 23-32 - [c47]Robert Dyer
, Hoan Anh Nguyen, Hridesh Rajan
, Tien N. Nguyen:
Boa: a language and infrastructure for analyzing ultra-large-scale software repositories. ICSE 2013: 422-431 - [c46]Hoan Anh Nguyen, Anh Tuan Nguyen, Tung Thanh Nguyen
, Tien N. Nguyen, Hridesh Rajan
:
A study of repetitiveness of code changes in software evolution. ASE 2013: 180-190 - [c45]Robert Dyer, Hoan Anh Nguyen, Hridesh Rajan
, Tien N. Nguyen:
Mining source code repositories with boa. SPLASH (Companion Volume) 2013: 13-14 - [c44]Eric Lin, Hridesh Rajan
:
Panini: a capsule-oriented programming language for implicitly concurrent program design. SPLASH (Companion Volume) 2013: 19-20 - [e6]Christoph Bockisch, Michael Haupt, Steve Blackburn, Hridesh Rajan, Joseph Gil:
VMIL@SPLASH '13: Proceedings of the 7th ACM workshop on Virtual machines and intermediate languages, Indianapolis, IN, USA, 28 October 2013. ACM 2013, ISBN 978-1-4503-2601-8 [contents] - 2012
- [j9]Tyler Sondag, Kian L. Pokorny, Hridesh Rajan
:
Frances: A Tool for Understanding Computer Architecture and Assembly Language. ACM Trans. Comput. Educ. 12(4): 14:1-14:31 (2012) - [c43]Robert Dyer
, Hridesh Rajan
, Yuanfang Cai:
An exploratory study of the design impact of language features for aspect-oriented interfaces. AOSD 2012: 143-154 - [c42]Rex D. Fernando, Robert Dyer, Hridesh Rajan
:
Event type polymorphism. FOAL 2012: 33-38 - [c41]Robert Dyer, Hoan Nguyen, Hridesh Rajan
, Tien N. Nguyen:
Analyzing ultra-large-scale code corpus with boa. SPLASH 2012: 25-26 - [c40]Robert Dyer, Hoan Nguyen, Hridesh Rajan
, Tien N. Nguyen:
Boa: analyzing ultra-large-scale code corpus. SPLASH 2012: 87-88 - [c39]Hridesh Rajan
, Michael Haupt, Christoph Bockisch, Stephen M. Blackburn
:
6th workshop on virtual machines and intermediate languages (VMIL'12). SPLASH 2012: 223-224 - 2011
- [c38]Hridesh Rajan
, Gary T. Leavens, Robert Dyer, Mehdi Bagherzadeh:
Modularizing crosscutting concerns with Ptolemy. AOSD (Companion) 2011: 61-62 - [c37]Mehdi Bagherzadeh, Hridesh Rajan
, Gary T. Leavens, Sean L. Mooney:
Translucid contracts: expressive specification and modular verification for aspect-oriented interfaces. AOSD 2011: 141-152 - [c36]Tyler Sondag, Hridesh Rajan
:
Phase-based tuning for better utilization of performance-asymmetric multicore processors. CGO 2011: 11-20 - [c35]