Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories

TL;DR

This paper presents a semi-automated two-phase approach combining meta-search and machine learning to help developers retrieve and rank relevant JavaScript technologies from web repositories, addressing information overload and ranking challenges.

Contribution

It introduces a novel two-phase method that integrates meta-search and machine learning to improve retrieval and ranking of JS libraries for developers.

Findings

Achieved satisfactory retrieval accuracy from NPM.

Effective ranking of technologies based on developer criteria.

Enhanced decision-making process for selecting JS libraries.

Abstract

The selection of software technologies is an important but complex task. We consider developers of JavaScript (JS) applications, for whom the assessment of JS libraries has become difficult and time-consuming due to the growing number of technology options available. A common strategy is to browse software repositories via search engines (e.g., NPM, or Google), although it brings some problems. First, given a technology need, the engines might return a long list of results, which often causes information overload issues. Second, the results should be ranked according to criteria of interest for the developer. However, deciding how to weight these criteria to make a decision is not straightforward. In this work, we propose a two-phase approach for assisting developers to retrieve and rank JS technologies in a semi-automated fashion. The first-phase (ST-Retrieval) uses a meta-search technique for collecting JS technologies that meet the developer's needs. The second-phase (called ST-Rank), relies on a machine learning technique to infer, based on criteria used by other projects in the Web, a ranking of the output of ST-Retrieval. We evaluated our approach with NPM and obtained satisfactory results in terms of the accuracy of the technologies retrieved and the order in which they were ranked.