Automated Analysis of Feature Models

Abstract

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains.AAFM has been applied in different activities along the Software Product Line (SPL) process such as product configuration and derivation, reverse engineering or SPL testing. As the field evolves, there is a need to evaluate the trends in the area and discover where the AAFM is being applied. Systematic Literature Reviews (SLRs) and Systematic Mapping Study (SMS) are the main techniques used to crawl the knowledge in a scientific area and candidates to discover the aforementioned tendencies. While SLRs are suitable to summarize the state of a research area by providing mostly qualitative information, SMSs focus on providing quantitative information and a categorization of the corpus that enables the identification of trends and research opportunities.We present a SMS to identify the evolution and trends in the application of the AAFM since 2010. Concretely, we have performed a search on different databases of AAFM-related papers. We selected 423 primary sources (papers) that followed the defined inclusion and exclusion criteria. The primary sources were classified according to different variability facets that were found during the reading and key-wording phase. It is important to remark that before 2010, AAFM was not well defined and it was referenced using an amalgam of names and concepts. Therefore, we consider that in 2010 the concept of AAFM was coined and then used in different domains and scenarios. This paper studies how AAFM has been used since its definition.First, we found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published.We also observed that there are only a few industrial and real evidences of the application of AAFM techniques in most of the cases. We detect in detail where and when the papers have been published and who are the authors and institutions that are contributing to the field. We saw that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are presented. Finally, we devise some research opportunities and applications in the future as well as synergies with other research areas. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.The reader can find the full text of this paper at https://doi.org/10.1007/s00607-018-0646-1