Investigation of Requirements Interdependencies in Existing Techniques of Requirements Prioritization

Requirements prioritization (RP) is considered as a key role in producing a successful system by selecting the most important requirements to be released. Requirements interdependencies (RI) is one of the crucial aspects that need to be addressed in RP, since most of the requirements in reality are not independent and have dependencies between each other. Thus, ignoring RI in RP process may lead to produce inaccurate prioritization result which directly impacts the system’s success. In spite of this, little is known about the impact of RI, and obviously further research is urgently required to measure the RI in the RP techniques. Hence, this study aims to investigate and analyze the existence and the execution steps of handling RI in the existing RP techniques to improve the performance of techniques in generating accurate result and assist the researchers and practitioners to select the appropriate technique that can handle RI in prioritization process. The findings indicate that, out of 65 techniques, there are only 4 techniques that handle the RI. The result reveals that these four techniques still suffer from issues of manual process and heavily rely on the experts’ participation. Proposing a new technique is recommended to overcome the identified limitations

Supporting the Requirements Prioritization Process. A Machine Learning approach

Requirements prioritization plays a key role in the requirements engineering process, in particular with respect to critical tasks such as requirements negotiation and software release planning. This paper presents a novel framework which is based on a requirements prioritization process that interleaves human and machine activities, enabling for an accurate prioritization of requirements. Similarly to the Analytic Hierarchy Process (AHP) method, our framework adopts an elicitation process based on the acquisition of pairwise preferences. Differently from AHP, where scalability is a big issue, the framework enables a prioritization process even over a large set of requirements, thanks to the exploitation of machine learning techniques that induce requirements ranking approximations at run time, and to the use of a boolean metrics. Moreover the new approach allows to reduce the bias of a dominance hierarchy, a strategy introduced by AHP to deal with the scalability issue. The paper describes also a methodology for the experimental evaluation of the framework and discusses the results of a first set of experiments designed on a real case-study which shows that an high accuracy in the final ranking can be obtained within a limited elicitation effor