Pre-trained Model-based Actionable Warning Identification: A Feasibility Study

TL;DR

This study evaluates the feasibility of using pre-trained models for actionable warning identification in static code analysis, demonstrating significant performance improvements over traditional machine learning methods.

Contribution

First systematic investigation of PTMs for AWI, including extensive evaluation, analysis of workflow factors, and practical guidelines for future improvements.

Findings

PTMs outperform state-of-the-art ML-based approaches by 9.85% to 21.12%.

Impact of data preprocessing, training, and prediction on PTM performance analyzed.

Identified reasons for PTM underperformance and provided practical enhancement guidelines.

Abstract

Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly learn an AWI classifier from labeled warnings, are notably common. However, these approaches still face the problem of restricted performance due to the direct reliance on a limited number of labeled warnings to develop a classifier. Very recently, Pre-Trained Models (PTMs), which have been trained through billions of text/code tokens and demonstrated substantial success applications on various code-related tasks, could potentially circumvent the above problem. Nevertheless, the performance of PTMs on AWI has not been systematically investigated, leaving a gap in understanding their pros and cons. In this paper, we are the first to explore the feasibility of applying various PTMs for AWI. By conducting the extensive evaluation on 10K+ SpotBugs warnings from 10 large-scale and open-source projects, we observe that all studied PTMs are consistently 9.85%~21.12% better than the state-of-the-art ML-based AWI approaches. Besides, we investigate the impact of three primary aspects (i.e., data preprocessing, model training, and model prediction) in the typical PTM-based AWI workflow. Further, we identify the reasons for current PTMs' underperformance on AWI. Based on our findings, we provide several practical guidelines to enhance PTM-based AWI in future work.