155 citations · 309 across the 27 of their papers we have counts for
12 papers · 1 filter
Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Ramneet Singh, Sathvik Joel, Abhav Mehrotra +4
Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and…
Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository
Ajinkya Deshpande, Anmol Agarwal, Shashank Shet +4
LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks. However, the comple…
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness
Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi +2
Existing evaluation benchmarks of language models of code (code LMs) focus almost exclusively on whether the LMs can generate functionally-correct code. In real-world software engi…
CodePlan: Repository-level Coding using LLMs and Planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade +6
Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase,…
StaticFixer: From Static Analysis to Static Repair
Naman Jain, Shubham Gandhi, Atharv Sonwane +5
Static analysis tools are traditionally used to detect and flag programs that violate properties. We show that static analysis tools can also be used to perturb programs that satis…
GrACE: Generation using Associated Code Edits
Priyanshu Gupta, Avishree Khare, Yasharth Bajpai +6
Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits…
CodeQueries: A Dataset of Semantic Queries over Code
Surya Prakash Sahu, Madhurima Mandal, Shikhar Bharadwaj +3
Developers often have questions about semantic aspects of code they are working on, e.g., "Is there a class whose parent classes declare a conflicting attribute?". Answering them r…
Learning and Evaluating Contextual Embedding of Source Code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan +1
Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A signific…
Deep Learning for Bug-Localization in Student Programs
Rahul Gupta, Aditya Kanade, Shirish Shevade
Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-t…
Active Learning for Efficient Testing of Student Programs
Ishan Rastogi, Aditya Kanade, Shirish Shevade
In this work, we propose an automated method to identify semantic bugs in student programs, called ATAS, which builds upon the recent advances in both symbolic execution and active…
Semi-Supervised Verified Feedback Generation
Shalini Kaleeswaran, Anirudh Santhiar, Aditya Kanade +1
Students have enthusiastically taken to online programming lessons and contests. Unfortunately, they tend to struggle due to lack of personalized feedback when they make mistakes.…
MintHint: Automated Synthesis of Repair Hints
Shalini Kaleeswaran, Varun Tulsian, Aditya Kanade +1
Being able to automatically repair programs is an extremely challenging task. In this paper, we present MintHint, a novel technique for program repair that is a departure from most…