ACWRecommender: A Tool for Validating Actionable Warnings with Weak Supervision

TL;DR

ACWRecommender is a two-stage machine learning tool that improves the detection and prioritization of actionable warnings in static analysis, reducing false alarms and aiding developers in bug identification.

Contribution

This work introduces a novel two-stage framework using weak supervision and a large dataset to enhance actionable warning validation and bug recommendation in static analysis.

Findings

Outperforms baseline methods in F1-score for warning detection

Achieves higher nDCG and MRR for bug recommendation

Successfully validated real bugs in in-the-wild evaluation

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.