Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

TL;DR

This paper introduces a hybrid static and dynamic call-graph based metric for bug prediction in JavaScript, demonstrating that combining these metrics improves machine learning model performance in identifying buggy functions.

Contribution

The paper proposes a novel hybrid invocation metric for JavaScript bug prediction, combining static and dynamic analysis to enhance model accuracy over static metrics alone.

Findings

Hybrid metrics improve prediction performance by 2-10%.

Replacing static metrics with hybrid ones enhances model accuracy.

Using all metrics together yields the best results.

Abstract

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2-10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.