ACWRecommender: A Tool for Validating Actionable Warnings with Weak Supervision

• URL: http://arxiv.org/abs/2309.09721v1
• Date: Mon, 18 Sep 2023 12:35:28 GMT
• Title: ACWRecommender: A Tool for Validating Actionable Warnings with Weak Supervision
• Authors: Zhipeng Xue, Zhipeng Gao, Xing Hu, Shanping Li
• Abstract summary: Static analysis tools have gained popularity among developers for finding potential bugs, but their widespread adoption is hindered by the high false alarm rates. Previous studies proposed the concept of actionable warnings, and apply machine-learning methods to distinguish actionable warnings from false alarms. We propose a two-stage framework called ACWRecommender to automatically identify actionable warnings and recommend those with a high probability of being real bugs.
• Score: 10.040337069728569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Static analysis tools have gained popularity among developers for finding potential bugs, but their widespread adoption is hindered by the accomnpanying high false alarm rates (up to 90%). To address this challenge, previous studies proposed the concept of actionable warnings, and apply machine-learning methods to distinguish actionable warnings from false alarms. Despite these efforts, our preliminary study suggests that the current methods used to collect actionable warnings are rather shaky and unreliable, resulting in a large proportion of invalid actionable warnings. In this work, we mined 68,274 reversions from Top-500 Github C repositories to create a substantia actionable warning dataset and assigned weak labels to each warning's likelihood of being a real bug. To automatically identify actionable warnings and recommend those with a high probability of being real bugs (AWHB), we propose a two-stage framework called ACWRecommender. In the first stage, our tool use a pre-trained model, i.e., UniXcoder, to identify actionable warnings from a huge number of SA tool's reported warnings. In the second stage, we rerank valid actionable warnings to the top by using weakly supervised learning. Experimental results showed that our tool outperformed several baselines for actionable warning detection (in terms of F1-score) and performed better for AWHB recommendation (in terms of nDCG and MRR). Additionaly, we also performed an in-the-wild evaluation, we manually validated 24 warnings out of 2,197 reported warnings on 10 randomly selected projects, 22 of which were confirmed by developers as real bugs, demonstrating the practical usage of our tool.

Related papers

• FineWAVE: Fine-Grained Warning Verification of Bugs for Automated Static Analysis Tools [18.927121513404924]
  Automated Static Analysis Tools (ASATs) have evolved over time to assist in detecting bugs. Previous research efforts have explored learning-based methods to validate the reported warnings. We propose FineWAVE, a learning-based approach that verifies bug-sensitive warnings at a fine-grained granularity.
  arXiv  Detail & Related papers  (2024-03-24T06:21:35Z)
• Semi-supervised Open-World Object Detection [74.95267079505145]
  We introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD) We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-02-25T07:12:51Z)
• Quieting the Static: A Study of Static Analysis Alert Suppressions [7.324969824727792]
  We examine 1,425 open-source Java-based projects that utilize Findbugs or Spotbugs for warning-suppressing configurations and source code annotations. We find that although most warnings are suppressed, only a small portion of them get frequently suppressed. Findings underscore the need for better communication and education related to the use of static analysis tools.
  arXiv  Detail & Related papers  (2023-11-13T17:16:25Z)
• Robustness of Unsupervised Representation Learning without Labels [92.90480374344777]
  We propose a family of unsupervised robustness measures, which are model- and task-agnostic and label-free. We validate our results against a linear probe and show that, for MOCOv2, adversarial training results in 3 times higher certified accuracy.
  arXiv  Detail & Related papers  (2022-10-08T18:03:28Z)
• Tracking the Evolution of Static Code Warnings: the State-of-the-Art and a Better Approach [18.350023994564904]
  Static bug detection tools help developers detect problems in the code, including bad programming practices and potential defects. Recent efforts to integrate static bug detectors in modern software development, such as in code review and continuous integration, are shown to better motivate developers to fix the reported warnings on the fly.
  arXiv  Detail & Related papers  (2022-10-06T03:02:32Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• How to Find Actionable Static Analysis Warnings [28.866251060033537]
  We show that effective predictors of such warnings can be created by methods that adjust the decision boundary. For eight open-source Java projects (CASSANDRA, JMETER, COMMONS, LUCENE-SOLR, ANT, TOMCAT, DERBY) we achieve perfect test results on 4/8 datasets.
  arXiv  Detail & Related papers  (2022-05-21T04:47:02Z)
• ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking [95.6347501381882]
  Object detectors are found to be vulnerable to physical-world patch hiding attacks. We propose ObjectSeeker as a framework for building certifiably robust object detectors.
  arXiv  Detail & Related papers  (2022-02-03T19:34:25Z)
• Sample-Efficient Safety Assurances using Conformal Prediction [57.92013073974406]
  Early warning systems can provide alerts when an unsafe situation is imminent. To reliably improve safety, these warning systems should have a provable false negative rate. We present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics.
  arXiv  Detail & Related papers  (2021-09-28T23:00:30Z)
• Assessing Validity of Static Analysis Warnings using Ensemble Learning [4.05739885420409]
  Static Analysis (SA) tools are used to identify potential weaknesses in code and fix them in advance, while the code is being developed. These rules-based static analysis tools generally report a lot of false warnings along with the actual ones. We propose a Machine Learning (ML)-based learning process that uses source codes, historic commit data, and classifier-ensembles to prioritize the True warnings.
  arXiv  Detail & Related papers  (2021-04-21T19:39:20Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)

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.