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.

Reactive programming reacts to data items as they occur, rather than waiting for them to complete. This programming paradigm is widely used in asynchronous and event-driven scenarios, such as web applications, microservices, real-time data processing, IoT, interactive UIs, and big data. When done right, it can offer greater responsiveness without extra resource usage. However, this also requires a thorough understanding of asynchronous and non-blocking coding, posing a learning curve for developers new to this style of programming. In this work, we analyze issues reported in reactive applications and explore their corresponding fixes. Our investigation results reveal that (1) developers often do not fix or ignore reactiveness bugs as compared to other bug types, and (2) this tendency is most pronounced for blocking-call bugs -- bugs that block the execution of the program to wait for the operations (typically I/O operations) to finish, wasting CPU and memory resources. To improve the debugging practice of such blocking bugs, we develop a pattern-based proactive program repair technique and obtain 30 patches, which we submit to the developers. In addition, we hypothesize that the low patch acceptance rate for reactiveness bugs is due to the difficulty of assessing the patches. This is in contrast to functionality bugs, where the correctness of the patches can be assessed by running test cases. To assess our hypothesis, we split our patches into two groups: one with performance improvement evidence and the other without. It turns out that the patches are more likely to be accepted when submitted with performance improvement evidence.

Modern web applications often resort to application development frameworks such as React, Vue.js, and Angular. While the frameworks facilitate the development of web applications with several useful components, they are inevitably vulnerable to unmanaged memory consumption since the frameworks often produce Single Page Applications (SPAs). Web applications can be alive for hours and days with behavior loops, in such cases, even a single memory leak in a SPA app can cause performance degradation on the client side. However, recent debugging techniques for web applications still focus on memory leak detection, which requires manual tasks and produces imprecise results. We propose Leakpair,a technique to repair memory leaks in single page applications. 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. To generate effective patches, Leakpairfollows the idea of pattern-based program repair since the automated repair strategy shows successful results in many recent studies. We evaluate the technique on more than 20 open-source projects without using explicit leak detection. The patches generated by our technique are also submitted to the projects as pull requests. The results show that Leakpaircan 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 Leakpaircan generate non-intrusive patches for memory leaks.