Exact Analysis for Next Release Problem

Abstract

Abstract-In software engineering, determining the set of requirements to implement in the next release is a critical foundation for the success of a project. Inappropriately including or excluding requirements may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. In the meantime, uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and could lead to unsound requirement decisions. To ease the impact of uncertainty in the software development process, it is important to provide techniques that explicitly manage uncertainty in requirements analysis and optimisation.This proposed research aims to provide a decision support framework for analysing uncertainty in requirements selection and optimisation. The proposed research involves three stages. Firstly, a simulation optimisation technique is introduced to model requirements uncertainty in requirements optimisation. Then, an exact technique is designed to eliminate the algorithmic uncertainty. Lastly, a probabilistic uncertainty analysis is applied to help the decision maker to understand requirement uncertainty propagation and the characteristics of requirements in requirements selection process.

Efficient anytime algorithms to solve the bi-objective Next Release Problem

“…The bi-objective Next Release Problem has been solved in the past using metaheuristic algorithms [15,31,32,33,34,56] are provably e cient (metaheuristics cannot guarantee that the solutions found are e cient) and they can be faster. In our previous report [11] it was clear that anytime methods outperform NSGA-II, GRASP and ACO, both in runtime and quality of solutions.…”

“…This approach has been used in the past by Li et al [35], and they conclude that the use of exact algorithms (like the proposed in this work) is important to avoid algorithmic uncertainty. • Sensitivity analysis and uncertainty, recently studies for the problem by Li et al [33,34] • While the requirements selection is a problem to be solved every few months using a traditional waterfall methodology, in agile methodologies the sprints usually last for one or two weeks, and the selection of requirements (user stories) for a sprint is something done every one or two weeks. Thus, the time to solve the problem should be accordingly short compared to the duration of the sprint.…”

“…There are some works that propose combining search techniques with human preferences [4,15,22], learning algorithms [3], statistical methods to deal with uncertainty [33,34] and AHP [50].…”