Papers by Saikat Chakraborty
AVATAR: A Parallel Corpus for Java-Python Program Translation (2023.findings-acl)
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| Challenge: | Program translation is a time-consuming and costly process that requires expertise in both the source and target languages. |
| Approach: | They present a collection of 9,515 programming problems and their solutions written in Java and Python. |
| Outcome: | The proposed model lacks in generating functionally accurate code. |
Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences (2025.acl-long)
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| Challenge: | Existing methods to improve security of LLM generated code are ineffective and lack localized regions of code. |
| Approach: | They propose a method for distilling a preference dataset of insecure and secure code pairs from frontier LLMs and a security reasoning that explains the issues and the fix. |
| Outcome: | The proposed method reduces code insecurity while improving overall code quality. |
Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
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| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
Ranking LLM-Generated Loop Invariants for Program Verification (2023.findings-emnlp)
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Saikat Chakraborty, Shuvendu Lahiri, Sarah Fakhoury, Akash Lal, Madanlal Musuvathi, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy
| Challenge: | Large Language Models (LLMs) are capable of synthesizing inductive loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariant. |
| Approach: | They propose a re-ranking approach to generate inductive loop invariants using Large Language Models . they propose reranking rankers that can distinguish between correct and incorrect attempts . |
| Outcome: | The proposed method reduces the number of calls to a verifier by comparing the generated results with the original model. |
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts (2022.acl-long)
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| Challenge: | Existing models that focus on identifying functional (dis)similarity of source code get confused when trying to identify functional (Dis)-similarities. |
| Approach: | They propose to pre-train a Transformer model with such automatically generated program contrasts to better identify similar code in the wild and differentiate vulnerable programs from benign ones. |
| Outcome: | The proposed model outperforms existing models in vulnerability and code clone detection tasks even with much less data. |
Building Language Models for Text with Named Entities (P18-1)
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| Challenge: | Existing language models fail to predict the entity names due to their wide variations. |
| Approach: | They propose a language model which can learn the entity names by leveraging their entity type information. |
| Outcome: | The proposed model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than state-of-the-art language models. |
A Transformer-based Approach for Source Code Summarization (2020.acl-main)
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| Challenge: | Generating a readable summary that describes the functionality of a program is known as source code summarization. |
| Approach: | They propose a Transformer model that uses a self-attention mechanism to capture long-range dependencies by encoding source code tokens relative to the code token position. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by a significant margin. |
Retrieval Augmented Code Generation and Summarization (2021.findings-emnlp)
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| Challenge: | Software developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. |
| Approach: | They propose a retrieval augmented framework that retrieves relevant code or summaries from a database and provides them as a supplement to code generation or summarization models. |
| Outcome: | The proposed framework can search for relevant code or summaries from retrieval databases and can work with unimodal (only code or natural language description) or bimodal instances (code-description pairs). |
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages (2023.eacl-main)
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| Challenge: | Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been effective for a broad spectrum of downstream software engineering tasks. |
| Approach: | They propose to combine a source-to-target model with a target-tosource model trained in parallel. |
| Outcome: | The proposed approach performs competitively with state-of-the-art methods. |