ToffA-DAS: an approach to conduct trade-off analysis for dynamically adaptable software

Abstract

The Dynamic Software Product Lines (DSPL) engineering processes aim to design Dynamically Adaptable Software (DAS) by increasing the flexibility for the generation of a huge number of configurations. It results in a software configuration space explosion making the analysis more difficult and complicating the developers work. In this sense, software engineers need to find a combination of systems features that can simultaneously satisfy constraints specified in feature and context models, Non-functional Requirements (NFRs), and stakeholders preferences. It means that they have to measure many configurations until finding the feasible ones, characterizing the product configuration process in a complex optimization problem. Most of the existing studies do not focus on the interactions between contextual information and NFRs when dealing with feature selection to meet the desired quality objectives in DAS. In addition, such studies do not use any planning strategy to support the configuration selection process. Based on these research gaps, we propose an approach that (i) manages the systems features and contexts; (ii) facilitates the understanding of how DSPL applications can behave from a certain context change, and (iii) enables to conduct trade-off analysis in order to find valid and feasible configurations, which meet the constraints and the interactions between contextual information and NFRs. Aiming to support the context variability modeling of DAS, we proposed the Extended Context-aware Feature Modeling (eCFM) technique to deal with constraints among contexts. Next, we defined the DAS Trade-off Analysis (ToffADAS) approach to deal with the configuration selection process embracing interactions between contextual information and NFRs. We also proposed a strategy to analyze context changes in order to define adaptation models for each prioritization of the systems features, contexts, and NFRs. Finally, we evolved our approach and named it as DAS Trade-off Analysis PLUS (ToffA-DAS+). ToffA-DAS is based on the integer linear programming technique, whereas ToffA-DAS+ uses a genetic algorithm. We performed a set of empirical studies in order to evaluate the proposal for this thesis. First, we conducted a survey to evaluate eCFM from the viewpoint of expressiveness to model the context constraints and easiness of use. Indeed, the analysis was focused on the comprehensibility of contextual variability modeling. Next, we performed a study based on simulations to gather initial evidence about the feasibility of using ToffA-DAS. It is based on how to conduct trade-off analysis and define adaptation models from feasible configurations found in the analysis. We also conducted an exploratory study to evaluate how the configurations obtained by the execution of ToffA-DAS affect the overall satisfaction level of NFRs. Finally, we evaluated the evolution of our approach in comparison with the previous release. As a result of the first study, the eCFM was considered a technique with a great expressiveness to represent adaptation rules among contexts and system features, besides the easiness of use and organization with the grouping of contexts. Therefore, we argue that the software engineers may take into account the use of eCFM technique to model DAS. In the second study, ToffA-DAS presented consistent results in accordance with the real-world scenarios and satisfied the estimated utility values and model constraints. The third study showed that the set of configurations generated by ToffA-DAS execution provide high satisfaction levels of NFRs. In the last study, we collected evidence that ToffA-DAS+ suggests more solutions from then possible valid configurations of the model. Based on the aforementioned studies, we evidenced that our approach can be handy when software engineers need assistance in the understanding of how to design a variety of configurable options for DSPL applications. It is based on the principle that each configuration option must be feasible to meet certain contextual changes without losing service quality. With the usage of our approach, software engineers can exhaustively analyze and simulate a solution before implementing it.