6 papers
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…
Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences
Mohammad Saqib Hasan, Saikat Chakraborty, Santu Karmaker +1
LLM generated code often contains security issues. We address two key challenges in improving secure code generation. First, obtaining high quality training data covering a broad s…
ClassInvGen: Class Invariant Synthesis using Large Language Models
Chuyue Sun, Viraj Agashe, Saikat Chakraborty +5
Formal program specifications in the form of preconditions, postconditions, and class invariants have several benefits for the construction and maintenance of programs. They not on…
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…
Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming
Saikat Chakraborty, Gabriel Ebner, Siddharth Bhat +4
Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite th…
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,…