Proactive Debugging of Memory Leakage Bugs in Single Page Web Applications

Abstract

Developing modern web applications often relies on web-based application frameworks such as React, Vue.js, and Angular. Although the frameworks accelerate the development of web applications with several useful and predefined components, they are inevitably vulnerable to unmanaged memory consumption as the frameworks often produce monolithic web pages, socalled, Single Page Applications (SPAs), in which no page refresh actions are made during navigation.Web applications can be alive for hours and days with behavior loops, in such cases, even a single memory leak in an SPA can cause performance degradation on the client side. However, recent debugging techniques for web applications focus on memory leak detection, which requires manual tasks and produces imprecise results, rather than proactively repairing memory leaks. We propose LEAKPAIR, a technique to proactively repair memory leaks in SPAs rather than following a classical and reactive debugging process. Given the insight that memory leaks are mostly non-functional bugs and fixing them might not change the behavior of an application, the technique is designed to proactively generate patches to fix memory leaks, without leak detection, which is often heavy and tedious. Thus, the proactive technique can significantly reduce the time and effort necessary to fix the memory leaks. To generate effective patches, LEAKPAIR follows the idea of pattern-based program repair since the automated repair strategy shows successful results in many recent studies. We extensively evaluate the technique on 60 open-source projects without using explicit leak detection. The patches generated by our technique are also submitted to the projects as pull requests (PRs). The results of PRs show that LEAKPAIR can generate effective patches to reduce memory consumption that are acceptable to developers. In addition, we execute the test suites given by the projects after applying the patches, and it turns out that the patches do not cause any functionality breakage; this might imply that LEAKPAIR can generate non-intrusive patches for memory leaks. Furthermore, we compare the performance of LEAKPAIR with that of GPT-4 as recent studies show that large language models are successful with program repair tasks. Our results show that our technique outperforms the language model.