A Recommender System for Recovering Relevant JavaScript Packages from Web Repositories

When developing JavaScript (JS) applications, the assessment of JS packages has become a difficult and time-consuming task for developers, due to the growing number of technology options available. Given a technology need, a common developers’ strategy is to browse software repositories via search engines (e.g., NPM, Google) and identify candidate JS packages. However, these engines might return a long list of results, which often causes information overloading issues in the developer. Furthermore, the results should be ranked according to the developer’s criteria, but weighting the available criteria to choose a JS package is not straightforward. To address these problems, we propose a two-phase recommender system for assisting developers in retrieving and ranking JS packages in a semi-automated fashion. The first phase uses a meta-search technique for collecting JS packages that meet the developer’s needs. Based on criteria used by other projects on the Web, the second phase applies a machine learning technique to infer a ranking of relevant packages for the output of the first phase. We performed an initial evaluation of our approach with the NPM package repository and obtained satisfactory results in terms of both the accuracy of the retrieved packages and the quality of the ranking for the developers.