46 citations · 130 across the 13 of their papers we have counts for
10 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…