2 research outputs found
Incorporating user preferences in multi-objective feature selection in software product lines using multi-criteria decision analysis
Software Product Lines Engineering has created various tools that assist with the standardisation in the design and implementation of clusters of equivalent software systems with an explicit representation of variability choices in the form of Feature Models, making the selection of the most ideal software product a Feature Selection problem. With the increase in the number of properties, the problem needs to be defined as a multi-objective optimisation where objectives are considered independently one from another with the goal of finding and providing decision-makers a large and diverse set of non-dominated solutions/products. Following the optimisation, decision-makers define their own (often complex) preferences on how does the ideal software product look like. Then, they select the unique solution that matches their preferences the most and discard the rest of the solutionsâsometimes with the help of some Multi-Criteria Decision Analysis technique. In this work, we study the usability and the performance of incorporating preferences of decision-makers by carrying-out Multi-Criteria Decision Analysis directly within the multi-objective optimisation to increase the chances of finding more solutions that match preferences of the decision-makers the most and avoid wasting execution time searching for non-dominated solutions that are poor with respect to decision-makersâ preferences
Preliminary study of multi-objective features selection for evolving software product lines
When dealing with software-intensive systems, it is often beneficial
to consider families of similar systems together. A common task is then to identify
the particular product that best fulfils a given set of desired product properties.
Software Product Lines Engineering (SPLE) provides techniques to design,
implement and evolve families of similar systems in a systematic fashion, with
variability choices explicitly represented, e.g., as Feature Models. The problem
of picking the âbestâ product then becomes a question of optimising the Feature
Configuration. When considering multiple properties at the same time, we have
to deal with multi-objective optimisation, which is even more challenging.
While change and evolution of software systems is the common case, to the best
of our knowledge there has been no evaluation of the problem of multi-objective
optimisation of evolving Software Product Lines. In this paper we present a benchmark
of large scale evolving Feature Models and we study the behaviour of the
state-of-the-art algorithm (SATIBEA). In particular, we show that we can improve
both the execution time and the quality of SATIBEA by feeding it with the
previous configurations: our solution converges nearly 10 times faster and gets an
113% improvement after one generation of genetic algorithm