Enriching Automatic Test Case Generation by Extracting Relevant Test Inputs from Bug Reports

• URL: http://arxiv.org/abs/2312.14898v2
• Date: Wed, 19 Mar 2025 20:06:00 GMT
• Title: Enriching Automatic Test Case Generation by Extracting Relevant Test Inputs from Bug Reports
• Authors: Wendkûuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawendé F. Bissyandé,
• Abstract summary: We introduce BRMiner, a novel approach that leverages Large Language Models (LLMs) in combination with traditional techniques to extract relevant inputs from bug reports.<n>In this study, we evaluate BRMiner using the Defects4J benchmark and test generation tools such as EvoSuite and Randoop.<n>Our results demonstrate that BRMiner achieves a Relevant Input Rate (RIR) of 60.03% and a Relevant Input Extraction Accuracy Rate (RIEAR) of 31.71%.
• Score: 10.587260348588064
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The quality of software is closely tied to the effectiveness of the tests it undergoes. Manual test writing, though crucial for bug detection, is time-consuming, which has driven significant research into automated test case generation. However, current methods often struggle to generate relevant inputs, limiting the effectiveness of the tests produced. To address this, we introduce BRMiner, a novel approach that leverages Large Language Models (LLMs) in combination with traditional techniques to extract relevant inputs from bug reports, thereby enhancing automated test generation tools. In this study, we evaluate BRMiner using the Defects4J benchmark and test generation tools such as EvoSuite and Randoop. Our results demonstrate that BRMiner achieves a Relevant Input Rate (RIR) of 60.03% and a Relevant Input Extraction Accuracy Rate (RIEAR) of 31.71%, significantly outperforming methods that rely on LLMs alone. The integration of BRMiner's input enhances EvoSuite ability to generate more effective test, leading to increased code coverage, with gains observed in branch, instruction, method, and line coverage across multiple projects. Furthermore, BRMiner facilitated the detection of 58 unique bugs, including those that were missed by traditional baseline approaches. Overall, BRMiner's combination of LLM filtering with traditional input extraction techniques significantly improves the relevance and effectiveness of automated test generation, advancing the detection of bugs and enhancing code coverage, thereby contributing to higher-quality software development.

Related papers

• From Requirements to Test Cases: An NLP-Based Approach for High-Performance ECU Test Case Automation [0.5249805590164901]
  This study investigates the use of Natural Language Processing techniques to transform natural language requirements into structured test case specifications. A dataset of 400 feature element documents was used to evaluate both approaches for extracting key elements such as signal names and values. The Rule-Based method outperforms the NER method, achieving 95% accuracy for more straightforward requirements with single signals.
  arXiv  Detail & Related papers  (2025-05-01T14:23:55Z)
• AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models [86.83875864328984]
  We propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi. Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models' reasoning abilities.
  arXiv  Detail & Related papers  (2025-02-24T07:02:31Z)
• Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models [20.71745514142851]
  RetriGen is a retrieval-augmented deep assertion generation approach. We conduct experiments to evaluate RetriGen against six state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-02-22T04:17:04Z)
• Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation [69.62857948698436]
  Recent advances in large language models (LLMs) have improved their performance on coding benchmarks. However, improvement is plateauing due to the exhaustion of readily available high-quality data. We propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity.
  arXiv  Detail & Related papers  (2025-02-20T18:32:19Z)
• Boundary Value Test Input Generation Using Prompt Engineering with LLMs: Fault Detection and Coverage Analysis [3.249891166806818]
  This paper presents a framework for assessing the effectiveness of large language models (LLMs) in generating boundary value test inputs for white-box software testing. Our analysis shows the strengths and limitations of LLMs in boundary value generation, particularly in detecting common boundary-related issues. This research provides insights into the role of LLMs in boundary value testing, underscoring both their potential and areas for improvement in automated testing methods.
  arXiv  Detail & Related papers  (2025-01-24T12:54:19Z)
• CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design [6.414167153186868]
  We propose CorrectBench, an automatic testbench generation framework with functional self-validation and self-correction. The proposed approach can validate the correctness of the generated testbenches with a success rate of 88.85%. Our work's performance is 62.18% higher than previous work in sequential tasks and almost 5 times the pass ratio of the direct method.
  arXiv  Detail & Related papers  (2024-11-13T10:45:19Z)
• Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing [31.327835928133535]
  Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance. It is crucial to conduct robustness testing on LAPR techniques before their practical deployment. We propose MT-LAPR, a Metamorphic Testing framework exclusively for LAPR techniques.
  arXiv  Detail & Related papers  (2024-10-10T01:14:58Z)
• Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests [4.574205608859157]
  We introduce UTGen, which combines search-based software testing and large language models to enhance the understandability of automatically generated test cases. We observe that participants working on assignments with UTGen test cases fix up to 33% more bugs and use up to 20% less time when compared to baseline test cases.
  arXiv  Detail & Related papers  (2024-08-21T15:35:34Z)
• Improving LLM-based Unit test generation via Template-based Repair [8.22619177301814]
  Unit test is crucial for detecting bugs in individual program units but consumes time and effort. Large language models (LLMs) have demonstrated remarkable reasoning and generation capabilities. In this paper, we propose TestART, a novel unit test generation method.
  arXiv  Detail & Related papers  (2024-08-06T10:52:41Z)
• Leveraging Large Language Models for Efficient Failure Analysis in Game Development [47.618236610219554]
  This paper proposes a new approach to automatically identify which change in the code caused a test to fail. The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure. Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year.
  arXiv  Detail & Related papers  (2024-06-11T09:21:50Z)
• LLM-Powered Test Case Generation for Detecting Tricky Bugs [30.82169191775785]
  AID generates test inputs and oracles targeting plausibly correct programs. We evaluate AID on two large-scale datasets with tricky bugs: TrickyBugs and EvalPlus. The evaluation results show that the recall, precision, and F1 score of AID outperform the state-of-the-art by up to 1.80x, 2.65x, and 1.66x, respectively.
  arXiv  Detail & Related papers  (2024-04-16T06:20:06Z)
• GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? [50.53312866647302]
  HateCheck is a suite for testing fine-grained model functionalities on synthesized data. We propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch. Crowd-sourced annotation demonstrates that the generated test cases are of high quality.
  arXiv  Detail & Related papers  (2024-02-23T10:02:01Z)
• Observation-based unit test generation at Meta [52.4716552057909]
  TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests.
  arXiv  Detail & Related papers  (2024-02-09T00:34:39Z)
• Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Models [4.318319522015101]
  Existing approaches produce test cases that either can be qualified as simple (e.g. unit tests) or that require precise specifications. Most testing procedures still rely on test cases written by humans to form test suites. We investigate the feasibility of performing this generation by leveraging large language models (LLMs) and using bug reports as inputs.
  arXiv  Detail & Related papers  (2023-10-10T05:30:12Z)
• Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing [13.743062498008555]
  We introduce MuTAP for improving the effectiveness of test cases generated by Large Language Models (LLMs) in terms of revealing bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs) Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets.
  arXiv  Detail & Related papers  (2023-08-31T08:48:31Z)
• Towards Automatic Generation of Amplified Regression Test Oracles [44.45138073080198]
  We propose a test oracle derivation approach to amplify regression test oracles. The approach monitors the object state during test execution and compares it to the previous version to detect any changes in relation to the SUT's intended behaviour.
  arXiv  Detail & Related papers  (2023-07-28T12:38:44Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future [63.99570204416711]
  We reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets. We define a uniform evaluation setup including a new formalization of the annotation error detection task. We release our datasets and implementations in an easy-to-use and open source software package.
  arXiv  Detail & Related papers  (2022-06-05T22:31:45Z)
• SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning [62.997667081978825]
  Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems. We present SUPERNOVA, a system responsible for test selection and defect prevention while also functioning as an automation hub. The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title.
  arXiv  Detail & Related papers  (2022-03-10T00:47:46Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining [69.00394670035747]
  Hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques.
  arXiv  Detail & Related papers  (2019-08-28T13:12:30Z)

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.