Automated Repair of Declarative Software Specifications in the Era of
Large Language Models
- URL: http://arxiv.org/abs/2310.12425v2
- Date: Tue, 7 Nov 2023 17:06:06 GMT
- Title: Automated Repair of Declarative Software Specifications in the Era of
Large Language Models
- Authors: Md Rashedul Hasan, Jiawei Li, Iftekhar Ahmed, Hamid Bagheri
- Abstract summary: We assess the effectiveness of utilizing OpenAI's ChatGPT to repair software specifications written in the Alloy declarative language.
Our study revealed that while ChatGPT falls short in comparison to existing techniques, it was able to successfully repair bugs that no other technique could address.
- Score: 12.995301758524935
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The growing adoption of declarative software specification languages, coupled
with their inherent difficulty in debugging, has underscored the need for
effective and automated repair techniques applicable to such languages.
Researchers have recently explored various methods to automatically repair
declarative software specifications, such as template-based repair,
feedback-driven iterative repair, and bounded exhaustive approaches. The latest
developments in large language models provide new opportunities for the
automatic repair of declarative specifications. In this study, we assess the
effectiveness of utilizing OpenAI's ChatGPT to repair software specifications
written in the Alloy declarative language. Unlike imperative languages,
specifications in Alloy are not executed but rather translated into logical
formulas and evaluated using backend constraint solvers to identify
specification instances and counterexamples to assertions. Our evaluation
focuses on ChatGPT's ability to improve the correctness and completeness of
Alloy declarative specifications through automatic repairs. We analyze the
results produced by ChatGPT and compare them with those of leading automatic
Alloy repair methods. Our study revealed that while ChatGPT falls short in
comparison to existing techniques, it was able to successfully repair bugs that
no other technique could address. Our analysis also identified errors in
ChatGPT's generated repairs, including improper operator usage, type errors,
higher-order logic misuse, and relational arity mismatches. Additionally, we
observed instances of hallucinations in ChatGPT-generated repairs and
inconsistency in its results. Our study provides valuable insights for software
practitioners, researchers, and tool builders considering ChatGPT for
declarative specification repairs.
Related papers
- Revisiting Evolutionary Program Repair via Code Language Model [11.711739409758476]
This paper introduces ARJA-CLM, which integrates the multiobjective evolutionary algorithm with CLM to fix multilocation bugs in Java projects.
We also propose a context-aware prompt construction stratege, which enriches the prompt with additional information about accessible fields and methods for the CLM generating candidate statements.
arXiv Detail & Related papers (2024-08-20T01:57:45Z) - FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking [1.985242455423935]
'FactCheck Editor' is an advanced text editor designed to automate fact-checking and correct factual inaccuracies.
It supports over 90 languages and utilizes transformer models to assist humans in the labor-intensive process of fact verification.
arXiv Detail & Related papers (2024-04-30T11:55:20Z) - A Deep Dive into Large Language Models for Automated Bug Localization and Repair [12.756202755547024]
Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR)
In this study, we take a deep dive into automated bug fixing utilizing LLMs.
This methodological separation of bug localization and fixing using different LLMs enables effective integration of diverse contextual information.
Toggle achieves the new state-of-the-art (SOTA) performance on the CodeXGLUE code refinement benchmark.
arXiv Detail & Related papers (2024-04-17T17:48:18Z) - An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications [5.395614997568524]
This paper presents a systematic investigation into the capacity of Large Language Models (LLMs) for repairing declarative specifications in Alloy.
We propose a novel repair pipeline that integrates a dual-agent LLM framework, comprising a Repair Agent and a Prompt Agent.
Our study reveals that LLMs, particularly GPT-4 variants, outperform existing techniques in terms of repair efficacy, albeit with a marginal increase in runtime and token usage.
arXiv Detail & Related papers (2024-04-17T03:46:38Z) - A Novel Approach for Automatic Program Repair using Round-Trip
Translation with Large Language Models [50.86686630756207]
Research shows that grammatical mistakes in a sentence can be corrected by translating it to another language and back.
Current generative models for Automatic Program Repair (APR) are pre-trained on source code and fine-tuned for repair.
This paper proposes bypassing the fine-tuning step and using Round-Trip Translation (RTT): translation of code from one programming language to another programming or natural language, and back.
arXiv Detail & Related papers (2024-01-15T22:36:31Z) - Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples.
One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports.
Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
arXiv Detail & Related papers (2023-11-11T11:01:13Z) - ChatGPT for Vulnerability Detection, Classification, and Repair: How Far
Are We? [24.61869093475626]
Large language models (LLMs) like ChatGPT exhibited remarkable advancement in a range of software engineering tasks.
We compare ChatGPT with state-of-the-art language models designed for software vulnerability purposes.
We found that ChatGPT achieves limited performance, trailing behind other language models in vulnerability contexts by a significant margin.
arXiv Detail & Related papers (2023-10-15T12:01:35Z) - RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic
Program Repair [75.40584530380589]
We propose a novel Retrieval-Augmented Patch Generation framework (RAP-Gen)
RAP-Gen explicitly leveraging relevant fix patterns retrieved from a list of previous bug-fix pairs.
We evaluate RAP-Gen on three benchmarks in two programming languages, including the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks in Java.
arXiv Detail & Related papers (2023-09-12T08:52:56Z) - Teaching Large Language Models to Self-Debug [62.424077000154945]
Large language models (LLMs) have achieved impressive performance on code generation.
We propose Self- Debugging, which teaches a large language model to debug its predicted program via few-shot demonstrations.
arXiv Detail & Related papers (2023-04-11T10:43:43Z) - TextFlint: Unified Multilingual Robustness Evaluation Toolkit for
Natural Language Processing [73.16475763422446]
We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint)
It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis.
TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
arXiv Detail & Related papers (2021-03-21T17:20:38Z) - On the Robustness of Language Encoders against Grammatical Errors [66.05648604987479]
We collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data.
Results confirm that the performance of all tested models is affected but the degree of impact varies.
arXiv Detail & Related papers (2020-05-12T11:01:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.