1. Home
  2. JUITA Vol. 10 No. 2, November 2022
  3. Delima

Requirements Conflict Detection and Resolution in AREM Using Intelligence System Approach

Rosa Delima, Retantyo Wardoyo, Khabib Mustofa

Abstract


Requirements engineering (RE) is the process of defining user requirements that are used as the main reference in the system development process. The quality of the RE results is measured based on the consistency and completeness of the requirements. The collection of requirements from multiple stakeholders can cause requirements conflict and have an impact on the inconsistency and incompleteness of the resulting requirements model. In this study, a method for automatic conflict detection and resolution in the Automatic Requirements Engineering Model (AREM) was developed. AREM is a model that automates the process of elicitation, analysis, validation, and requirements specification. The requirement conflict detection method was developed using an intelligent agent approach combined with a Weighted Product approach. Meanwhile, Conflict resolution is made automatically using a rule-based model and clustering method. Testing the ability of the method to detect and resolve conflicting requirements was carried out through five data sets of requirements from five system development projects. Based on the test results, it is known that the system is able to produce a set of objects that have conflicts in the data requirements. For conflict resolution, experiments were conducted with five conflict resolution scenarios. The experimental results show that the method is able to resolve conflicts by producing the highest completeness value, but the results of conflict resolution also produce a number of soft goals. The success of the method in detecting and resolving conflicts in the model is able to overcome the problem of inconsistencies and incompleteness in the requirements model.


Keywords


Requirements engineering; AREM; conflict detection; conflict resolution; intelligence agent; weight product; rule-based; K-means clustering

References


[1] I. Sommerville, Software Engineering Ninth Edition, Ninth Edit. United States of America: Addison Wesley, 2011.

[2] A. van Lamsweerde, Requirements Engineering From System Goals to UML Models to Software Specifications. Wiley, 2009.

[3] M. Aldekhail and D. Ziani, “Intelligent Method for Software Requirement Conflicts Identification and Removal: Proposed Framework and Analysis,” IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 17, no. 12, pp. 91–98, 2017.

[4] M. Aldekhail, A. Chikh, and D. Ziani, “Software Requirements Conflict Identification: Review and Recommendations,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 10, pp. 326–335, 2016, doi: 10.14569/ijacsa.2016.071044.

[5] W. Guo, L. Zhang, and X. Lian, “Automatically detecting the conflicts between software requirements based on finer semantic analysis,” arXiv, vol. abs/2103.0, 2021, [Online]. Available: http://arxiv.org/abs/2103.02255

[6] A. Van Lamsweerde, R. Darimont, and E. Letier, “Managing conflicts in goal-driven requirements engineering,” IEEE Trans. Softw. Eng., vol. 24, no. 11, pp. 908–926, 1998, doi: 10.1109/32.730542.

[7] R. Delima, R. Wardoyo, and K. Mustofa, “Goal-Oriented Requirements Engineering: State of the Art and Research Trend,” JUITA J. Inform., vol. 9, no. 1, pp. 105–114, 2021, doi: 10.30595/juita.v9i1.9827.

[8] A. van Lamsweerde and E. Letier, “Integrating obstacles in Goal-Driven Requirements Engineering,” in Proceedings - International Conference on Software Engineering, 1998, no. April, pp. 53–62.

[9] A. Van Lamsweerde, “Handling obstacles in goal-oriented requirements engineering,” IEEE Trans. Softw. Eng., vol. 26, no. 10, pp. 978–1005, 2000, doi: 10.1109/32.879820.

[10] D. Alrajeh, J. Kramer, A. Van Lamsweerde, A. Russo, and S. Uchitel, “Generating obstacle conditions for requirements completeness,” in Proceedings - International Conference on Software Engineering, 2012, pp. 705–715. doi: 10.1109/ICSE.2012.6227147.

[11] A. Cailliau and A. van Lamsweerde, “Assessing requirements-related risks through probabilistic goals and obstacles,” Requir. Eng., vol. 18, no. 2, pp. 129–146, 2013, doi: 10.1007/s00766-013-0168-5.

[12] S. Zardari, R. Bahsoon, and A. Ekárt, “Cloud adoption: Prioritizing obstacles and obstacles resolution tactics using AHP,” Proc. ACM Symp. Appl. Comput., pp. 1013–1020, 2014, doi: 10.1145/2554850.2555067.

[13] A. Cailliau and A. Van Lamsweerde, “Runtime Monitoring and Resolution of Probabilistic Obstacles to System Goals,” Proc. - 2017 IEEE/ACM 12th Int. Symp. Softw. Eng. Adapt. Self-Managing Syst. SEAMS 2017, pp. 1–11, 2017, doi: 10.1109/SEAMS.2017.5.

[14] R. Degiovanni, N. Ricci, D. Alrajehy, P. Castro, and N. Aguirre, “Goal-conflict detection based on temporal satisfiability checking,” ASE 2016 - Proc. 31st IEEE/ACM Int. Conf. Autom. Softw. Eng., pp. 507–518, 2016, doi: 10.1145/2970276.2970349.

[15] R. Degiovanni, P. Castro, M. Arroyo, M. Ruiz, N. Aguirre, and M. Frias, “Goal-conflict likelihood assessment based on model counting,” Proc. - Int. Conf. Softw. Eng., pp. 1125–1135, 2018, doi: 10.1145/3180155.3180261.

[16] R. Degiovanni, G. Regis, F. Molina, and N. Aguirre, “A genetic algorithm for goal-conflict identification,” ASE 2018 - Proc. 33rd ACM/IEEE Int. Conf. Autom. Softw. Eng., pp. 520–531, 2018, doi: 10.1145/3238147.3238220.

[17] M. B. Rehman, H. M. E. I. Dafallaa, N. Ahmad, I. Ahmad, M. Rashid, and R. Khan, “Requirement elicitation: Requirements conflict resolution and communication model for Telecommunication Sector,” in Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 2021, pp. 105–122. doi: 10.4108/eai.27-2-2020.2303293.

[18] Q. Khan, M. A. Khan, Q. Javaid, I. Ullah, K. Ullah, and M. Fawad, “A Rule Based Genetic Algorithm Technique for Conflicts Resolution in Requirements Engineering,” vol. 13, no. 11, pp. 8427–8433, 2016, doi: 10.1166/jctn.2016.5993.

[19] S. Russell and P. Norvig, Artificial Intelligence A Modern Approach Fourth Edition, Fourth. Pearson Series in Artificial Intelligence, 2020.

[20] A. Kolios, V. Mytilinou, E. Lozano-Minguez, and K. Salonitis, “A comparative study of multiple-criteria decision-making methods under stochastic inputs,” Energies, vol. 9, no. 7, pp. 1–21, 2016, doi: 10.3390/en9070566.

[21] R. Cahyanto, A. R. Chrismanto, and D. Sebastian, “Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 3, pp. 531–540, 2020, doi: 10.28932/jutisi.v6i3.3006.

[22] P. S. Mann, “Introductory Statistics, 8th Ed,” John Wiley Sons, Inc., vol. 8, no. 11, p. 736, 2012.


Full Text: PDF

DOI: 10.30595/juita.v10i2.14855

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2579-8901