Automated repair of internationalization presentation failures in web pages using style similarity clustering and search-based techniques

Internationalization enables companies to reach a global audience by adapting their websites to locale specific language and content. However, such translations can often introduce Internationalization Presentation Failures (IPFs) - distortions in the intended appearance of a website. It is challenging for developers to design websites that can inherently adapt to varying lengths of text from different languages. Debugging and repairing IPFs is complicated by the large number of HTML elements and CSS properties that define a web page's appearance. Tool support is also limited as existing techniques can only detect IPFs, with the repair remaining a labor intensive manual task. To address this problem, we propose a search-based technique for automatically repairing IPFs in web applications. Our empirical evaluation showed that our approach was able to successfully resolve 98% of the reported IPFs for 23 real-world web pages. In a user study, participants rated the visual quality of our fixes significantly higher than the unfixed versions

Exploring Plausible Patches Using Source Code Embeddings in JavaScript

Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the so-called Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle. The latter, however, might not give a reliable result, because of the imperfections in such oracles; one of which is usually the test suite. Although (re-) running the test suite is right under one's nose, in real life applications the problem of over- and underfitting often occurs, resulting in inadequate patches. Efforts that have been made to tackle with this problem include patch filtering, test suite expansion, careful patch producing and many more. Most approaches to date use post-filtering relying either on test execution traces or make use of some similarity concept measured on the generated patches. Our goal is to investigate the nature of these similarity-based approaches. To do so, we trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it. These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program. We analyzed these similarity lists and found that plain document embeddings may lead to misclassification - it fails to capture nuanced code semantics. Nevertheless, in some cases it also provided useful information, thus helping to better understand the area of Automated Program Repair.Comment: Paper accepted in APR2021 conferenc