Baishakhi Ray

6.8k total citations · 4 hit papers
81 papers, 3.6k citations indexed

About

Baishakhi Ray is a scholar working on Information Systems, Software and Artificial Intelligence. According to data from OpenAlex, Baishakhi Ray has authored 81 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Information Systems, 41 papers in Software and 26 papers in Artificial Intelligence. Recurrent topics in Baishakhi Ray's work include Software Engineering Research (49 papers), Software Testing and Debugging Techniques (35 papers) and Advanced Malware Detection Techniques (19 papers). Baishakhi Ray is often cited by papers focused on Software Engineering Research (49 papers), Software Testing and Debugging Techniques (35 papers) and Advanced Malware Detection Techniques (19 papers). Baishakhi Ray collaborates with scholars based in United States, United Kingdom and China. Baishakhi Ray's co-authors include Suman Jana, Kexin Pei, Yuchi Tian, Prémkumar Dévanbu, Saikat Chakraborty, Vladimir Filkov, Miryung Kim, Daryl Posnett, Kai-Wei Chang and Wasi Uddin Ahmad and has published in prestigious journals such as Communications of the ACM, IEEE Access and IEEE Transactions on Software Engineering.

In The Last Decade

Baishakhi Ray

72 papers receiving 3.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DeepTest

2018 786 citations Yuchi Tian, Kexin Pei et al. profile →
Unified Pre-training for Program Understanding and Generation

2021 387 citations Wasi Uddin Ahmad, Saikat Chakraborty et al. profile →
A large scale study of programming languages and code quality in github

2014 236 citations Baishakhi Ray, Vladimir Filkov et al. profile →
Gender and Tenure Diversity in GitHub Teams

2015 233 citations Baishakhi Ray, Prémkumar Dévanbu et al. profile →

Peers

Baishakhi Ray

IS

AI

SOFTW

CNC

SP

Abram Hindle Canada

Annibale Panichella Netherlands

Gail E. Kaiser United States

Alexander Pretschner Germany

Zhi Jin China

William G. J. Halfond United States

Lori Pollock United States

Li Li China

Miryung Kim United States

Phil McMinn United Kingdom

Abram Hindle Canada

Baishakhi Ray

2.9k ×1.5

IS

857 ×0.6

AI

1.2k ×0.9

SOFTW

1.2k ×1.5

CNC

686 ×0.9

SP

Citations per year, relative to Baishakhi Ray Baishakhi Ray (= 1×) peers Abram Hindle

Countries citing papers authored by Baishakhi Ray

Since Specialization

Citations

This map shows the geographic impact of Baishakhi Ray's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Baishakhi Ray with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Baishakhi Ray more than expected).

Fields of papers citing papers by Baishakhi Ray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Baishakhi Ray. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Baishakhi Ray. The network helps show where Baishakhi Ray may publish in the future.

Co-authorship network of co-authors of Baishakhi Ray

This figure shows the co-authorship network connecting the top 25 collaborators of Baishakhi Ray. A scholar is included among the top collaborators of Baishakhi Ray based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Baishakhi Ray. Baishakhi Ray is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Rahman, Shanto, et al.. (2025). UTFix: Change Aware Unit Test Repairing using LLM. Proceedings of the ACM on Programming Languages. 9(OOPSLA1). 143–168.

2.

Nitin, Vikram, et al.. (2025). C2SaferRust: Transforming C Projects Into Safer Rust With NeuroSymbolic Techniques. IEEE Transactions on Software Engineering. 52(2). 618–630. 1 indexed citations

3.

Ding, Yangruibo, et al.. (2024). TRACED: Execution-aware Pre-training for Source Code. 1–12. 11 indexed citations

4.

Arora, Sanjay, et al.. (2024). Yuga: Automatically Detecting Lifetime Annotation Bugs in the Rust Language. IEEE Transactions on Software Engineering. 50(10). 2602–2613.

5.

Wei, Xiaokai, Sujan K. Gonugondla, Wasi Uddin Ahmad, et al.. (2023). Towards Greener Yet Powerful Code Generation via Quantization: An Empirical Study. 224–236. 14 indexed citations

6.

Ahmad, Wasi Uddin, Saikat Chakraborty, Baishakhi Ray, & Kai-Wei Chang. (2023). Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages. 1528–1542. 6 indexed citations

7.

Wang, Shiqi, Zheng Li, Haifeng Qian, et al.. (2023). ReCode: Robustness Evaluation of Code Generation Models. 13818–13843. 22 indexed citations

8.

Kumar, Varun, et al.. (2023). A Static Evaluation of Code Completion by Large Language Models. 347–360. 5 indexed citations

9.

Ding, Yangruibo, et al.. (2022). Towards Learning (Dis)-Similarity of Source Code from Program Contrasts. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 6300–6312. 19 indexed citations

10.

Mao, Chengzhi, et al.. (2019). Unrestricted Adversarial Attacks For Semantic Segmentation. arXiv (Cornell University). 1 indexed citations

11.

Tian, Yuchi, Ziyuan Zhong, Vicente Ordóñez, & Baishakhi Ray. (2019). Testing Deep Neural Network based Image Classifiers.. arXiv (Cornell University). 3 indexed citations

12.

She, Dongdong, et al.. (2018). NEUZZ: Efficient Fuzzing with Neural Program Learning. arXiv (Cornell University). 15 indexed citations

13.

Liu, Chao, et al.. (2018). Poster: A Recommender System for Developer Onboarding. International Conference on Software Engineering. 2 indexed citations

14.

Tian, Yuchi, et al.. (2018). DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars. IEEE Conference Proceedings. 2018. 303–314. 2 indexed citations

15.

Chakraborty, Saikat, Miltiadis Allamanis, & Baishakhi Ray. (2018). Tree2Tree Neural Translation Model for Learning Source Code Changes.. arXiv (Cornell University). 11 indexed citations

16.

Casalnuovo, Casey, et al.. (2017). GitcProc: a tool for processing and classifying GitHub commits. 396–399. 20 indexed citations

17.

Jana, Suman, et al.. (2016). Automatically Detecting Error Handling Bugs Using Error Specifications. USENIX Security Symposium. 345–362. 28 indexed citations

18.

Ray, Baishakhi, et al.. (2016). On the "naturalness" of buggy code. Zurich Open Repository and Archive (University of Zurich). 428–439. 160 indexed citations

19.

Casalnuovo, Casey, et al.. (2015). Assert use in GitHub projects. International Conference on Software Engineering. 1. 755–766. 29 indexed citations

20.

McDonnell, Tyler, Baishakhi Ray, & Miryung Kim. (2013). An Empirical Study of API Stability and Adoption in the Android Ecosystem. 70–79. 226 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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