arXiv subjects

Reem Aleithan

Publications and source records attributed to Reem Aleithan.

4 recordsLinked to original sources

arXiv2024

SWE-Bench+: Enhanced Coding Benchmark for LLMs

Large Language Models (LLMs) in Software Engineering (SE) can offer assistance for coding. To facilitate a rigorous evaluation of LLMs in practical coding contexts, Carlos et al. introduced the SWE-bench dataset, which comprises 2,294 real-world GitHub issues and their corresponding pull requests, collected from 12 widely used Python repositories. Several impressive LLM-based toolkits recently are developed and evaluated on this dataset. However, a systematic evaluation of the quality of SWE-bench remains missing. In this paper, we addressed this gap by presenting an empirical analysis of the SWE-bench dataset. We conducted a manual screening of instances where SWEAgent + GPT-4 successfully resolved issues by comparing the model-generated patches with the actual pull requests. SWE-Agent+GPT-4 was at the top of SWE-bench leaderboard during the time of our study. Our analysis reveals some critical issues with the SWE-bench dataset: 1) 32.67% of the successful patches involve cheating as the solutions were directly provided in the issue report or the comments. We refer to as solution leakage problem. 2) 31.08% of the passed patches are suspicious patches due to weak test cases, i.e., the tests were not adequate to verify the correctness of a patch. When we filtered out these problematic issues, the resolution rate of SWE-Agent+GPT-4 dropped from 12.47% to 3.97%. We also observed that the same data quality issues also exist in the two variants of SWE-bench, i.e., SWE-bench Lite and SWE-Bench Verified. In addition, over 94% of the issues were created before LLM's knowledge cutoff dates, posing potential data leakage issues.

cs.SE↗

arXiv2024

Retrieval-Augmented Test Generation: How Far Are We?

Retrieval Augmented Generation (RAG) has advanced software engineering tasks but remains underexplored in unit test generation. To bridge this gap, we investigate the efficacy of RAG-based unit test generation for machine learning (ML/DL) APIs and analyze the impact of different knowledge sources on their effectiveness. We examine three domain-specific sources for RAG: (1) API documentation (official guidelines), (2) GitHub issues (developer-reported resolutions), and (3) StackOverflow Q&As (community-driven solutions). Our study focuses on five widely used Python-based ML/DL libraries, TensorFlow, PyTorch, Scikit-learn, Google JAX, and XGBoost, targeting the most-used APIs. We evaluate four state-of-the-art LLMs -- GPT-3.5-Turbo, GPT-4o, Mistral MoE 8x22B, and Llama 3.1 405B -- across three strategies: basic instruction prompting, Basic RAG, and API-level RAG. Quantitatively, we assess syntactical and dynamic correctness and line coverage. While RAG does not enhance correctness, RAG improves line coverage by 6.5% on average. We found that GitHub issues result in the best improvement in line coverage by providing edge cases from various issues. We also found that these generated unit tests can help detect new bugs. Specifically, 28 bugs were detected, 24 unique bugs were reported to developers, ten were confirmed, four were rejected, and ten are awaiting developers' confirmation. Our findings highlight RAG's potential in unit test generation for improving test coverage with well-targeted knowledge sources. Future work should focus on retrieval techniques that identify documents with unique program states to optimize RAG-based unit test generation further.

cs.SE↗

arXiv2023

SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models

We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans source code and reports specific types of bugs, 2) an LLM-based false-positive filter that can identify false-positive bugs in the results of static bug detectors (e.g., the result of step 1) to improve detection accuracy, and 3) an LLM-based patch generator that can generate patches for the detected bugs above. As a proof-of-concept, SkipAnalyzer is built on ChatGPT, which has exhibited outstanding performance in various software engineering tasks. To evaluate SkipAnalyzer, we focus on two types of typical and critical bugs that are targeted by static bug detection, i.e., Null Dereference and Resource Leak as subjects. We employ Infer to aid the gathering of these two bug types from 10 open-source projects. Consequently, our experiment dataset contains 222 instances of Null Dereference bugs and 46 instances of Resource Leak bugs. Our study demonstrates that SkipAnalyzer achieves remarkable performance in the mentioned static analysis tasks, including bug detection, false-positive warning removal, and bug repair. In static bug detection, SkipAnalyzer achieves accuracy values of up to 68.37% for detecting Null Dereference bugs and 76.95% for detecting Resource Leak bugs, improving the precision of the current leading bug detector, Infer, by 12.86% and 43.13%, respectively. For removing false-positive warnings, SkipAnalyzer can reach a precision of up to 93.88% for Null Dereference bugs and 63.33% for Resource Leak bugs. Additionally, SkipAnalyzer surpasses state-of-the-art false-positive warning removal tools. Furthermore, in bug repair, SkipAnalyzer can generate syntactically correct patches to fix its detected bugs with a success rate of up to 97.30%.

cs.SE↗

arXiv2021

Explainable Software Defect Prediction: Are We There Yet?

Explaining the prediction results of software defect prediction models is a challenging while practical task, which can provide useful information for developers to understand and fix the predicted bugs. To address this issue, recently, Jiarpakdee et al. proposed to use {two state-of-the-art} model-agnostic techniques (i.e., LIME and BreakDown) to explain the prediction results of bug prediction models. Their experiments show these tools can generate promising results and the generated explanations can assist developers understand the prediction results. However, the fact that LIME and BreakDown were only examined on a single software defect prediction model setting calls into question about their consistency and reliability across software defect prediction models with various settings. In this paper, we set out to investigate the consistency and reliability of model-agnostic technique based explanation generation approaches (i.e., LIME and BreakDown) on software defect prediction models with different settings , e.g., different data sampling techniques, different machine learning classifiers, and different prediction scenarios. Specifically, we use both LIME and BreakDown to generate explanations for the same instance under software defect prediction models with different settings and then check the consistency of the generated explanations for the instance. We reused the same defect data from Jiarpakdee et al. in our experiments. The results show that both LIME and BreakDown generate inconsistent explanations under different software defect prediction settings for the same test instances, which makes them unreliable for explanation generation. Overall, with this study, we call for more research in explainable software defect prediction towards achieving consistent and reliable explanation generation.

cs.SE↗