Previous work has established both the importance and difficulty of establishing and maintaining adequate software traceability. While it has been shown to support essential maintenance and evolution tasks, recovering traceability links between related software artifacts is a time consuming and error prone task. As such, substantial research has been done to reduce this barrier to adoption by at least partially automating traceability link recovery. In particular, recent work has shown that supervised machine learning can be effectively used for automating traceability link recovery, as long as there is sufficient data (i.e., labeled traceability links) to train a classification model. Unfortunately, the amount of data required by these techniques is a serious limitation, given that most software systems rarely have traceability information to begin with. In this paper we address this limitation of previous work and propose an approach based on active learning, which substantially reduces the amount of training data needed by supervised classification approaches for traceability link recovery while maintaining similar performance.

Previous studies have shown that software traceability, the ability to link together related artifacts from different sources within a project (e.g., source code, use cases, documentation, etc.), improves project outcomes by assisting developers and other stakeholders with common tasks such as impact analysis, concept location, etc. Establishing traceability links in a software system is an important and costly task, but only half the struggle. As the project undergoes maintenance and evolution, new artifacts are added and existing ones are changed, resulting in outdated traceability information. Therefore, specific steps need to be taken to make sure that traceability links are maintained in tandem with the rest of the project. In this paper we address this problem and propose a novel approach called TRAIL for maintaining traceability information in a system. The novelty of TRAIL stands in the fact that it leverages previously captured knowledge about project traceability to train a machine learning classifier which can then be used to derive new traceability links and update existing ones. We evaluated TRAIL on 11 commonly used traceability datasets from six software systems and compared it to seven popular Information Retrieval (IR) techniques including the most common approaches used in previous work. The results indicate that TRAIL outperforms all IR approaches in terms of precision, recall, and F-score.

Software development video tutorials are emerging as a new resource for developers to support their information needs. However, when trying to find the right video to watch for a task at hand, developers have little information at their disposal to quickly decide if they found the right video or not. This can lead to missing the best tutorials or wasting time watching irrelevant ones.

Automatic software categorization is the task of assigning categories or tags to software libraries in order to summarize their functionality. Correctly assigning these categories is essential to ensure that relevant libraries can be easily retrieved by developers from large repositories. Current categorization approaches rely on the semantics reflected in the source code, or use supervised machine learning techniques, which require a set of labeled software as a training data. These approaches fail when such information is not available. We propose a novel unsupervised approach for the automatic categorization of Java libraries, which uses the bytecode of a library in order to determine its category. We show that the approach is able to successfully categorize libraries from the Apache Foundation Repository.

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant software can be easily retrieved by developers from large repositories. State of the art approaches either rely on the availability of the source code, or use supervised machine learning approaches, which require a set of already labeled software as training data. These restrictions make current approaches fail when such information is not available. We propose a novel approach, which overcomes these limitations by using semantic information recovered from byte code and an unsupervised algorithm to assign categories to software systems. We evaluated our approach in a study on the Apache Foundation Repository of Java libraries and the results indicate that our approach is able to correctly identify a correct category for 86% of the libraries.