Intelligent Identification and Resolution of Software Requirement Conflicts: Assessment and Evaluation

Abstract

Considerable research has demonstrated how effective requirements engineering is critical for the success of software projects. Requirements engineering has been established and recognized as one of the most important aspects of software engineering as of late. It is noteworthy to mention that requirement consistency is a critical factor in project success, and conflicts in requirements lead to waste of cost, time, and effort. A considerable number of research studies have shown the risks and problems caused by working with requirements that are in conflict with other requirements. These risks include running overtime or over budget, which may lead to project failure. At the very least, it would result in the extra expended effort. Various studies have also stated that failure in managing requirement conflicts is one of the main reasons for unsuccessful software projects due to high cost and insufficient time. Many prior research studies have proposed manual techniques to detect conflicts, whereas other research recommends automated approaches based on human analysis. Moreover, there are different resolutions for conflicting requirements. Our previous work proposed a scheme for dealing with this problem using a novel intelligent method to detect conflicts and resolve them. A rule-based system was proposed to identify conflicts in requirements, and a genetic algorithm (GA) was used to resolve conflicts. The objective of this work is to assess and evaluate the implementation of the method of minimizing the number of conflicts in the requirements. The methodology implemented comprises two different stages. The first stage, detecting conflicts using a rule-based system, demonstrated a correct result with 100% accuracy. The evaluation of using the GA to resolve and reduce conflicts in the second stage also displayed a good result and achieved the desired goal as well as the main objective of the research.

Requirements Conflict Detection and Resolution in AREM Using Intelligence System Approach

“…[17], who reviewed and applied communication and negotiation in conflict resolution. The same thing was also done by Maysoon and Djamal [3]; they reviewed a number of methods which were categorized into two conflict resolution techniques, namely negotiation between stakeholders and prioritizing requirements. Both of these techniques resolve conflicts manually by prioritizing the role of analysis systems in conflict resolution.…”

“…Both of these techniques resolve conflicts manually by prioritizing the role of analysis systems in conflict resolution. For automatic conflict resolution, there are studies that apply rule-based and genetic algorithms methods [3,18].…”

“…The goal is the main element that represents the requirements of the stakeholders. Conflicting goals between stakeholders will affect the consistency and completeness of the model [3][4]. Therefore, AREM must be able to detect conflicts between elements in the data requirements and followed by requirements conflict resolution.…”

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

“…• Requirements Traceability: Requirements traceability is used to capture the relationships between the requirements, the design, and the implementation of a system [55]. We recognized 21 papers related to this task, part of which focused on detecting the relationships between requirements (inter-dependency between requirements) [56,57,58,59,60,61,62,63,64,65,66], while the remaining focused on detecting the relationships between requirements and other artifacts (design documents and source code) [67,55,68,69,70,71,72,73,74].…”

Optimization of Intelligent Evaluation of English Diagnosis System Based on Particle Swarm Optimization

“…There are many theories about particle swarm optimization and English diagnosis system. For example, some scientists have proposed a distributed task allocation algorithm for multi-agent systems based on particle swarm optimization, which can solve conflicts [1][2]. Some scientists try to use population algorithms and genetic algorithms to generate test copies [3][4].…”