Preface to the special issue on software evolution, adaptability and variability
Welcome to this special issue on Software Evolution, Adaptability and Variability. Software evolution is the term used in software engineering to refer to the process of developing an initial version of the software and then repeatedly updating it to satisfy changing user requirements. Software evolution is an inevitable activity: successful and long-living software applications do not only have to cope with changes in their application domain, their success also stimulates users to request new and improved features [1][2][3]. In addition, a single software implementation cannot always satisfy the various requirements demanded by different groups of users. One of the techniques to cope with such differing user requirements is variability, i.e., the introduction of variation points in the software's implementation that enable its instantiation in different contexts or for different purposes [4]. Another enabling technology to support software evolution is software adaptability, a way to leverage the current software investment by engineering adaptability into the system. Adaptable software implementations are prepared today to fit tomorrow's needs [5].
Software evolution is an important and complex research topic, especially because almost all software development organizations are exposed to it. Software systems are typically business critical and are required to run continuously and flawlessly. Software engineers are pressured to adjust existing systems to new business opportunities, emerging technologies, law changes, and so on. These changes are to be made in a cost-effective manner, without loss of quality of the existing functionality [1]. Effective software evolution therefore also comprises activities that focus on the implementation itself (e.g., engineering variability and adaptability into the system) and represents an investment in the future of the software's implementation.
This special issue originates from the BElgian-NEtherlands software eVOLution (BENEVOL) seminar, a yearly gathering of software evolution researchers from Belgium, the Netherlands and the surrounding countries like Luxembourg, Germany and France. Each year the BENEVOL seminar assembles around 40 researchers and features high-quality and highly innovative contributions in the larger area of software evolution, often leading to interesting discussions and collaborations between researchers.
This special issue showcases a number of recent advances in the area of software evolution. The open call for this special issue received 33 submissions and after two rounds of intense reviewing by at least 3 referees per submission, we are pleased to present you 12 of these papers in this special issue.
The topics covered in this special issue cover a wide variety of aspects that are highly relevant for software evolution in general and for software adaptability and variability in particular. We hope that readers will enjoy this special issue and through these papers gain useful insights into the larger domain of software evolution. We would like to extend our gratitude to all the authors who submitted papers to this special issue. Also, many thanks to the referees who ensured that all authors received valuable feedback on how to improve their work; their constructive feedback helped to shape the papers in this issue. Our thanks also go to Jan Bergstra, editor of the Science of Computer Programming journal, for hosting this special issue and to Bas van Vlijmen, editorial assistant of the Science of Computer Programming journal, for all his advice.
We now briefly introduce the twelve papers of this special issue. Arbuckle proposes a new method for measuring software evolution in terms of artefacts' shared information content in his paper entitled ''Studying software evolution using artefacts' shared information content''. In order to do this, Arbuckle uses a similarity value representing the quantity of information shared between artefact pairs. Similarity values for releases are then collated over the software's evolution to form a map quantifying change through lack of similarity. The method has general applicability: it can disregard otherwise salient software features such as programming paradigm, language or application domain because it considers software artefacts purely in terms of the mathematically justified concept of information content.