No abstract

Automatic recovery of test-to-code traceability links is an important task in many areas of software engineering, like quality assurance and code maintenance. The research community has shown great interest in such a topic and has developed several techniques that already made significant advances in the field. These techniques include text-based learning algorithms, of which corpus is built from the source code of the software components. Several techniques based on information retrieval have been benchmarked, but the capabilities of many learning algorithms have not yet been tested. In this work we examine the textual similarity measures produced by three different machine learning techniques for the recovery of traceability information while also considering various textual representations of the source code. The obtained results are evaluated on 4 open source systems based on naming conventions. We have been able to improve the current textual similarity based state-of-the-art results in the case of each evaluated system.

New customers often require custom features of a successfully marketed product. As the number of variants grow, new challenges arise in the maintenance and evolution activities. Software product line (SPL) architecture is a timely answer to these challenges. The SPL adoption however is a large one time investment that affects both technical and organizational issues. From the program code point of view, the extractive approach is appropriate when there are already several product variants. Analyzing the feature structure, the differences and commonalities of the variants lead to the new common architecture. In this work in progress paper we report initial experiments of feature extraction from a set of product variants written in the Magic fourth generation language (4GL). Since existing approaches are mostly designed for mainstream languages, we adapted and reused reverse engineering approaches to the 4GL environment. We followed a semi-automatic feature extraction method, where the higher level features are provided by domain experts. These features are then linked to the internal structure of Magic applications using a textual similarity (IR-based) method. We demonstrate the feasibility of 4GL feature extraction method and validate it on two variants of a real life logistical system each consisting of more than 2000 Magic programs.

Software product line (SPL) architecture facilitates systematic reuse to serve specic feature requests of new customers. Our work deals with the adoption of SPL architecture in an existing legacy system. In this case, the extractive approach of SPL adoption turned out to be the most viable method, where the system is redesigned keeping variants within the same code base. The analysis of the feature structure is a crucial point in this process as it involves both domain experts working at a higher level of abstraction and developers working directly on the program code. In this work, we propose an automatic method to extract feature-to-program connections starting from a very high level set of features provided by domain experts and existing program code. The extraction is performed by combining and further processing call graph information on the code with textual similarity between code and high level features. The context of our work is an industrial SPL adoption project of a large scale logistical information system written in an 4G language, Magic. We demonstrate the benets of the combined method and its use by dierent stakeholders in this project.