With the continuous improvement of software processes, it is possible to increase quality, to address different application domains and accelerate the development of software products. However, existing process modelling notations like the SPEM meta‐model typically do not have appropriate constructs for expressing process variability. Thus, SPEM‐based approaches such as SMartySPEM and vSPEM provide mechanisms for representing variabilities to address characteristics of different projects. Here, the authors empirically compared SMartySPEM and for variability representation in software processes models, aiming to analyze correction, time, and efficiency. The performed experiment provides preliminary evidence based on 11 participants and sample size N = 44. Participants took longer to comprehend diagrams in SMartySPEM. However, the correctness of SMartySPEM diagrams had a superior result and higher efficiency based on the median values and hypothesis tests. With regard to variability mechanisms, the diagrams modelled with SMartySPEM had a slightly lower efficiency and it took longer for modifying them, thus the correctness of the SMartySPEM diagrams had a superior result. Therefore, an initial body of knowledge indicated a positive efficiency of SMartySPEM for variability representation in process models, as well as potential improvements as the reduction in SMartySPEM diagrams complexity, which may contribute to its evolution.

Variability management is one of the most important activities during software product line development and evolution. Current literature presents several approaches for variability management, especially based on UML, such as, PLUS and SMarty. A systematic process with guidelines support SMarty. Existing literature for these kind of approaches provides slight experimental evidence of their effectiveness at product configuration. Thus, this is considered fundamental for transferring technology to the industry. This paper provides experimental evidence on the product configuration capability of SMarty by comparing it to PLUS, one of the most cited product-line method in literature. The experimental study provides incipient evidence that SMarty is more effective for resolving variabilities and configuring consistent products at UML class level. Thus, overall obtained results indicated the capability of SMarty at configuring specific products.

For nearly 30 years, industry and researchers have proposed many software variability tools to cope with the complexity of modeling variability in software development, followed by a number of publications on variability techniques built upon theoretical foundations. After more than 25 years of the practice of software variability, there are not many studies investigating the impact of software variability tools in the industry and the perception of practitioners. For this reason, we investigate in this research work how existing software variability tools fulfill the needs of companies demanding this kind of tool support. We conducted a survey with practitioners from companies in eight different countries in order to analyze the missing capabilities of software variability management tools and we compared the results of the survey with the scientifoc literature through a systematic mapping study (SMS) to analyze if the proposed solutions cover the needs required by practitioners. Our major findings indicate that many tools lack important qualities such as interoperability, collaborative work, code generation, scalability, impact analysis, and test; while the results from the SMS showed these such capabilities are, to some extent, found in some of the existing tools.