What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation

• URL: http://arxiv.org/abs/2412.00828v1
• Date: Sun, 01 Dec 2024 14:28:48 GMT
• Title: What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation
• Authors: Xin Yin, Chao Ni, Xiaodan Xu, Xiaohu Yang,
• Abstract summary: We propose Attention-based Self-guided Automatic Unit Test GenERation (AUGER) approach. AUGER contains two stages: defect detection and error triggering. It makes great improvements by 4.7% to 35.3% in terms of F1-score and Precision in defect detection. It can trigger 23 to 84 more errors than state-of-the-art (SOTA) approaches in unit test generation.
• Score: 3.8244417073114003
• License:
• Abstract: Software defects heavily affect software's functionalities and may cause huge losses. Recently, many AI-based approaches have been proposed to detect defects, which can be divided into two categories: software defect prediction and automatic unit test generation. While these approaches have made great progress in software defect detection, they still have several limitations in practical application, including the low confidence of prediction models and the inefficiency of unit testing models. To address these limitations, we propose a WYSIWYG (i.e., What You See Is What You Get) approach: Attention-based Self-guided Automatic Unit Test GenERation (AUGER), which contains two stages: defect detection and error triggering. In the former stage, AUGER first detects the proneness of defects. Then, in the latter stage, it guides to generate unit tests for triggering such an error with the help of critical information obtained by the former stage. To evaluate the effectiveness of AUGER, we conduct a large-scale experiment by comparing with the state-of-the-art (SOTA) approaches on the widely used datasets (i.e., Bears, Bugs.jar, and Defects4J). AUGER makes great improvements by 4.7% to 35.3% and 17.7% to 40.4% in terms of F1-score and Precision in defect detection, and can trigger 23 to 84 more errors than SOTAs in unit test generation. Besides, we also conduct a further study to verify the generalization in practical usage by collecting a new dataset from real-world projects.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis [4.554856650068748]
  Flaky tests, which pass or fail inconsistently without code changes, are a major challenge in software engineering. We aim to create an automated framework to detect flaky tests in quantum software.
  arXiv  Detail & Related papers  (2024-10-31T02:43:04Z)
• Test Generation Strategies for Building Failure Models and Explaining Spurious Failures [4.995172162560306]
  Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. We propose to build failure models for inferring interpretable rules on test inputs that cause spurious failures. We show that our proposed surrogate-assisted approach generates failure models with an average accuracy of 83%.
  arXiv  Detail & Related papers  (2023-12-09T18:36:15Z)
• Cal-DETR: Calibrated Detection Transformer [67.75361289429013]
  We propose a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR, UP-DETR and DINO. We develop an uncertainty-guided logit modulation mechanism that leverages the uncertainty to modulate the class logits. Results corroborate the effectiveness of Cal-DETR against the competing train-time methods in calibrating both in-domain and out-domain detections.
  arXiv  Detail & Related papers  (2023-11-06T22:13:10Z)
• 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)
• YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach [6.142308190194335]
  We propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns. We show that YOLOv8, a state-of-the-art neural network, achieves defect detection precisions of more than 0.9 mAP on our final dataset.
  arXiv  Detail & Related papers  (2023-07-28T12:17:01Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• 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)
• Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles [38.23896575179384]
  We propose a principled and practically effective framework that simultaneously addresses the two tasks. One instantiation reduces the estimation error for unsupervised accuracy estimation by at least 70% and improves the F1 score for error detection by at least 4.7%. On iWildCam, one instantiation reduces the estimation error for unsupervised accuracy estimation by at least 70% and improves the F1 score for error detection by at least 4.7%.
  arXiv  Detail & Related papers  (2021-06-29T21:32:51Z)
• TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task [80.38130122127882]
  TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE) In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? We find that label errors account for 8% absolute F1 test error, and that more than 50% of the examples need to be relabeled.
  arXiv  Detail & Related papers  (2020-04-30T15:07:37Z)

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.