Who's Who in SBSE

This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative.

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

Requirements engineering for multiple customers, each of whom have competing and often conflicting priorities, raises issues of negotiation, mediation and conflict resolution. This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space.

This paper is concerned with the Multi-Objective Next Release Problem (MONRP), a problem in search-based requirements engineering. Previous work has considered only single objective formulations. In the multi-objective formulation, there are at least two (possibly conflicting) objectives that the software engineer wishes to optimize. It is argued that the multi-objective formulation is more realistic, since requirements engineering is characterised by the presence of many complex and conflicting demands, for which the software engineer must find a suitable balance. The paper presents the results of an empirical study into the suitability of weighted and Pareto optimal genetic algorithms, together with the NSGA-II algorithm, presenting evidence to support the claim that NSGA-II is well suited to the MONRP. The paper also provides benchmark data to indicate the size above which the MONRP becomes non-trivial.