A Hybrid ant Colony Tabu Search Algorithm for Solving Next Release Problems
Balogun Abdullateef Oluwagbemiga1, Basri Shuib2, Said Jadid Abdulkadir3, Garba Mariam4, Almomani Malek Ahmad Thabeb5
1Balogun Abdullateef Oluwagbemiga, Department of Computer Science, University Technology, Seri Iskandar, Perak, Malaysia.
2Basri Shuib, Department of Computer Science, University Technology, Seri Iskandar, Perak, Malaysia.
3Said Jadid Abdulkadir, Department of Computer Science, University Technology, Seri Iskandar, Perak, Malaysia.
4Garba Mariam, Department of Computer Science, University Technology, Seri Iskandar, Perak, Malaysia.
5Almomani Malek Ahmad Thabeb, Department of Computer Science, University Technology, Seri Iskandar, Perak, Malaysia.
Manuscript received on 03 February 2019 | Revised Manuscript received on 10 February 2019 | Manuscript Published on 22 March 2019 | PP: 191-198 | Volume-8 Issue-5S April 2019 | Retrieval Number: ES3416018319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Next Release Problem (NRP) is a challenge in software engineering to define which set of requirements are to be developed in the next release of a software product taking in consideration several constraints such as the cost of development, user’s significance, and constraints related to scheduling, dependencies between requirements and available expertise. Solving this problem will help software engineers to make decisions on the set of requirements to include as features in the next release of a software product. This paper proposes a hybrid of Ant Colony Optimization (ACO) algorithm and Tabu Search (TS) for solving NRP using a cost-value model for requirements. A fitness function with two objectives was considered to maximize users’ satisfaction and to minimize the cost of developing the requirements requested by users. The hybrid Ant Colony Optimization Tabu Search (ACOTS) algorithm is based on Ant Colony Optimization (ACO) algorithm while it employs the history keeping strategy of Tabu Search (TS) when constructing new solutions (local search spaces) for each initial solution generated by each ant. The procedure of the hybrid algorithm starts by generating random solutions that serve as a pivot for all ants of the colony which is based on the pheromone information, the set objectives in the fitness function and problem specific local heuristic information associated with each of the objectives. The output of the hybrid ACOTS is a set of promising optimal values which are the total number of the set of requirements from which a subset is to be selected. The results of the experiments showed that the application of ACOTS yielded larger and better sets of results than existing methods (ACS, Ant System and Tabu Search). The application of ACOTS also enables an easier parameter tuning (budget, number of requirements).
Keywords: Requirement Engineering, Next Release Problem, Metaheuristics, Optimization.
Scope of the Article: Computer Science and Its Applications