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.

International audience

To characterize the building blocks of a legacy software system (e.g., structure, dependencies), programmers usually spend a long time navigating its source code. Yet, modern integrated development environments (IDEs) do not provide appropriate means to efficiently achieve complex software comprehension tasks. To deal with this unfulfilled need, we present Hunter, a tool for the visualization of JavaScript applications. Hunter visualizes source code through a set of coordinated views that include a node-link diagram that depicts the dependencies among the components of a system, and a treemap that helps programmers to orientate when navigating its structure.

Summary The manifestation of code anomalies in software systems often indicates symptoms of architecture degradation. Several approaches have been proposed to detect such anomalies in the source code. However, most of them fail to assist developers in prioritizing anomalies harmful to the software architecture of a system. This article presents an investigation on how developers, when supported by architecture blueprints, are able to prioritize architecturally relevant code anomalies. First, we performed a controlled experiment where participants explored both blueprints and source code to reveal architecturally relevant code anomalies. Although the use of blueprints has the potential to improve code anomaly prioritization, the participants often made several mistakes. We found these mistakes might occur because developers miss relationships between implementation and blueprint elements when they prioritize anomalies in an ad hoc manner. Furthermore, the time spent on the prioritization process was considerably high. Aiming to improve the accuracy and effectiveness of the process, we provided means to automate the prioritization process. In particular, we explored 3 prioritization criteria, which establish different ways of relating the blueprint elements with code anomalies. These criteria were implemented in the JSpIRIT tool. The approach was evaluated in the context of 2 applications with satisfactory precision results.

Code smells are a popular mechanism for identifying structural design problems in software systems. Several tools have emerged to support the detection of code smells and propose some refactorings. However, existing tools do not guarantee that a smell will be automatically fixed by means of refactorings. This article presents Bandago, an automated approach to fix a specific type of code smell called Brain Method . A Brain Method centralizes the intelligence of a class and manifests itself as a long and complex method that is difficult to understand and maintain by developers. For each Brain Method , Bandago recommends several refactoring solutions to remove the smell using a search strategy based on simulated annealing. Our approach has been evaluated with several open-source Java applications, and the results show that Bandago can automatically fix more than 60% of Brain Methods . Furthermore, we conducted a survey with 35 industrial developers that showed evidence about the usefulness of the refactorings proposed by Bandago. Also, we compared the performance of the Bandago against that of a third-party refactoring tool.