109 citations · 159 across the 10 of their papers we have counts for
5 papers · 1 filter
LLM For Loop Invariant Generation and Fixing: How Far Are We?
Mostafijur Rahman Akhond, Saikat Chakraborty, Gias Uddin
A loop invariant is a property of a loop that remains true before and after each execution of the loop. The identification of loop invariants is a critical step to support automate…
LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation
Sarah Fakhoury, Aaditya Naik, Georgios Sakkas +2
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, g…
Studying LLM Performance on Closed- and Open-source Data
Toufique Ahmed, Christian Bird, Premkumar Devanbu +1
Large Language models (LLMs) are finding wide use in software engineering practice. These models are extremely data-hungry, and are largely trained on open-source (OSS) code distri…
Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?
Madeline Endres, Sarah Fakhoury, Saikat Chakraborty +1
Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent. However,…
Interactive Code Generation via Test-Driven User-Intent Formalization
Shuvendu K. Lahiri, Sarah Fakhoury, Aaditya Naik +8
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, w…