Abstract
Requirements review is an effective technique to ensure the quality of requirements in practice, especially in safety-critical domains (e.g., avionics systems, automotive systems). In such contexts, a typical requirements review process often prioritizes requirements, due to limited time and monetary budget, by, for instance, prioritizing requirements with higher implementation cost earlier in the review process. However, such a requirement implementation cost is typically estimated by stakeholders who often lack knowledge about (future) requirements implementation scenarios, which leads to uncertainty in cost overrun. In this article, we explicitly consider such uncertainty (quantified as cost overrun probability) when prioritizing requirements based on the assumption that a requirement with higher importance, a higher number of dependencies to other requirements, and higher implementation cost will be reviewed with the higher priority. Motivated by this, we formulate four objectives for uncertainty-wise requirements prioritization: maximizing the importance of requirements, requirements dependencies, the implementation cost of requirements, and cost overrun probability. These four objectives are integrated as part of our search-based uncertainty-wise requirements prioritization approach with tool support, named as URP. We evaluated six Multi-Objective Search Algorithms (MOSAs) (i.e., NSGA-II, NSGA-III, MOCell, SPEA2, IBEA, and PAES) together with Random Search (RS) using three real-world datasets (i.e., the RALIC, Word, and ReleasePlanner datasets) and 19 synthetic optimization problems. Results show that all the selected MOSAs can solve the requirements prioritization problem with significantly better performance than RS. Among them, IBEA was over 40% better than RS in terms of permutation effectiveness for the first 10% of prioritized requirements in the prioritization sequence of all three datasets. In addition, IBEA achieved the best performance in terms of the convergence of solutions, and NSGA-III performed the best when considering both the convergence and diversity of nondominated solutions.
- Klaus Pohl. 2010. Requirements engineering: Fundamentals, principles, and techniques. Springer Publishing Company, IncGoogle ScholarDigital Library
- YangMing Zhu. 2016. Software Reading Techniques. Apress, Berkeley, CA.Google Scholar
- Norman Riegel and Joerg Doerr. 2015. A systematic literature review of requirements prioritization criteria. In Proceedings of the Conference on Requirements Engineering: Foundation for Software Quality (REFSQ’15). Lecture Notes in Computer Science, Vol 9013, S. Fricker and K. Schneider (Eds). Springer, Cham, 300--317. DOI:http://dx.doi.org/10.1007/978-3-319-16101-3_22Google ScholarCross Ref
- Jack Shih-Chieh Hsu, Chien-Lung Chan, Julie Yu-Chih Liu, and Houn-Gee Chen. 2008. The impacts of user review on software responsiveness: Moderating requirements uncertainty. Info. Manage. 45, 4 (2008), 203--210. DOI:http://dx.doi.org/10.1016/j.im.2008.01.006Google Scholar
- Bill Curtis, Herb Krasner, and Neil Iscoe. 1988. A field study of the software design process for large systems. Commun. ACM 31, 11 (1988), 1268--1287. DOI:http://dx.doi.org/10.1145/50087.50089Google ScholarDigital Library
- Ann M. Hickey and Alan M. Davis. 2004. A unified model of requirements elicitation. J. Manage. Info. Syst. 20, 4 (2004), 65--84. DOI:http://dx.doi.org/10.1080/07421222.2004.11045786Google ScholarDigital Library
- Christof Ebert and Jozef De Man. 2005. Requirements uncertainty: Influencing factors and concrete improvements. In Proceedings of the 27th International Conference on Software Engineering. ACM, 553--560. DOI:http://dx.doi.org/10.1145/1062455.1062554Google ScholarDigital Library
- Lingbo Li, Mark Harman, Fan Wu, and Yuanyuan Zhang. 2016. The value of exact analysis in requirements selection. IEEE Trans. Softw. Eng. 43, 6 (2017), 580--596. DOI:http://dx.doi.org/10.1109/TSE.2016.2615100Google ScholarDigital Library
- Emmanuel Letier, David Stefan, and Earl T. Barr. 2014. Uncertainty, risk, and information value in software requirements and architecture. In Proceedings of the 36th International Conference on Software Engineering. ACM, 883--894. DOI:http://dx.doi.org/10.1145/2568225.2568239Google Scholar
- Patrik Berander and Anneliese Andrews, 2005. Requirements prioritization. In Engineering and Managing Software Requirements, Aybüke Aurum and Claes Wohlin (Eds). Springer, 69--94.Google Scholar
- Andrea Herrmann and Maya Daneva. 2008. Requirements prioritization based on benefit and cost prediction: An agenda for future research. In Proceedings of the 16th IEEE International Requirements Engineering Conference. IEEE, 125--134. DOI:http://dx.doi.org/10.1109/RE.2008.48Google ScholarDigital Library
- Lingbo Li, Mark Harman, Emmanuel Letier, and Yuanyuan Zhang. 2014. Robust next release problem: Handling uncertainty during optimization. In Proceedings of the Annual Conference on Genetic and Evolutionary Computation. ACM, 1247--1254. DOI:http://dx.doi.org/10.1145/2576768.2598334Google ScholarDigital Library
- Olugbenga Jide Olaniran, Peter E. D. Love, David Edwards, Oluwole Alfred Olatunji, and Jane Matthews. 2015. Cost overruns in hydrocarbon megaprojects: A critical review and implications for research. Project Manage. J. 46, 6 (2015), 126--138. DOI:http://dx.doi.org/10.1002/pmj.21556Google Scholar
- Paul Baker, Mark Harman, Kathleen Steinhöfel, and Alexandros Skaliotis. 2006. Search based approaches to component selection and prioritization for the next release problem. In Proceedings of the International Conference on Software Maintenance. IEEE, 176--185. DOI:http://dx.doi.org/10.1109/ICSM.2006.56Google ScholarDigital Library
- Antônio Mauricio Pitangueira, Rita Suzana P. Maciel, and Márcio Barros. 2015. Software requirements selection and prioritization using sbse approaches: A systematic review and mapping of the literature. J. Syst. Softw. 103 (2015), 267--280. DOI:http://dx.doi.org/10.1016/j.jss.2014.09.038Google ScholarDigital Library
- Alan M. Davis. 2003. The art of requirements triage. IEEE Comput. 36, 3 (2003), 42--49. DOI:http://dx.doi.org/10.1109/MC.2003.1185216Google ScholarDigital Library
- John M. Hammersley and D. C. Handscomb. 1965. Monte Carlo Methods. Chapman and Hall, London.Google Scholar
- David Johnson. 1997. The triangular distribution as a proxy for the beta distribution in risk analysis. J. Roy. Stat. Soci.: Series D (The Statistician) 46, 3 (1997), 387--398. DOI:http://dx.doi.org/10.1111/1467-9884.00091Google ScholarCross Ref
- Juan J. Durillo and Antonio J. Nebro. 2011. Jmetal: A java framework for multi-objective optimization. Adv. Eng. Softw. 42, 10 (2011), 760--771. DOI:http://dx.doi.org/10.1016/j.advengsoft.2011.05.014Google ScholarDigital Library
- Soo Ling Lim. 2010. Social networks and collaborative filtering for large-scale requirements elicitation. Ph.D. Dissertation. University of New South Wales.Google Scholar
- Antonio Mauricio Pitangueira, Paolo Tonella, Angelo Susi, Rita Suzana Maciel, and Marcio Barros, 2016. Risk-aware multi-stakeholder next release planning using multi-objective optimization. In Requirements Engineering: Foundation for Software Quality. Refsq 2016. Lecture Notes in Computer Science, Vol 9619, M. Daneva and O. Pastor (Eds). Springer, Cham, 3--18. DOI:http://dx.doi.org/10.1007/978-3-319-30282-9_1Google Scholar
- Muhammad Rezaul Karim and Guenther Ruhe. 2014. Bi-objective genetic search for release planning in support of themes. In Proceedings of the Conference on Search-Based Software Engineering (SSBSE’14). Lecture Notes in Computer Science, Vol 8636, C. Le Goues and S. Yoo (Eds). Springer, Cham, 123--137. DOI:http://dx.doi.org/10.1007/978-3-319-09940-8_9Google ScholarCross Ref
- Victor R. Basili, Scott Green, Oliver Laitenberger, Filippo Lanubile, Forrest Shull, Sivert Sørumgård, and Marvin V Zelkowitz. 1996. The empirical investigation of perspective-based reading. Empir. Softw. Eng. 1 (1996), 133--164. DOI:http://dx.doi.org/10.1007/BF00368702Google ScholarCross Ref
- Yan Li, Man Zhang, Tao Yue, Shaukat Ali, and Li Zhang. 2017. Search-based uncertainty-wise requirements prioritization. In Proceedings of the 22nd International Conference on Engineering of Complex Computer Systems (ICECCS’17). IEEE, 80--89. DOI:http://dx.doi.org/10.1109/ICECCS.2017.11Google ScholarCross Ref
- Yuanyuan Zhang, Mark Harman, Gabriela Ochoa, Guenther Ruhe, and Sjaak Brinkkemper. 2018. An empirical study of meta-and hyper-heuristic search for multi-objective release planning. ACM Trans. Softw. Eng. Methodol. 27, 1 (2018), Article No.: 3. DOI:http://dx.doi.org/10.1145/3196831Google ScholarDigital Library
- Yuanyuan Zhang, Anthony Finkelstein, and Mark Harman. 2008. Search based requirements optimisation: Existing work and challenges. In Proceedings of the Conference on Requirements Engineering: Foundation for Software Quality (REFSQ’08). Lecture Notes in Computer Science, Vol 5025, B. Paech and C. Rolland (Eds). Springer, Berlin, 88--94. DOI:http://dx.doi.org/10.1007/978-3-540-69062-7_8Google ScholarDigital Library
- Abdel Salam Sayyad and Hany Ammar. 2013. Pareto-optimal search-based software engineering (posbse): A literature survey. In Proceedings of the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13) IEEE, San Francisco, CA, 21--27. DOI:http://dx.doi.org/10.1109/RAISE.2013.6615200Google ScholarCross Ref
- Shaukat Ali, Paolo Arcaini, Dipesh Pradhan, Safdar Aqeel Safdar, and Tao Yue. 2020. Quality indicators in search-based software engineering: An empirical evaluation. ACM Trans. Softw. Eng. Methodol. 29, 2 (2020), Article No. 10. DOI:http://dx.doi.org/10.1145/3375636Google ScholarDigital Library
- Mark Harman, S. Afshin Mansouri, and Yuanyuan Zhang. 2012. Search-based software engineering: Trends, techniques and applications. ACM Comput. Surveys 45, 1 (2012), Article No. 11. DOI:http://dx.doi.org/10.1145/2379776.2379787Google Scholar
- Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. A. M. T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: Nsga-Ii. IEEE Trans. Evolution. Comput. 6, 2 (2002), 182--197. DOI:http://dx.doi.org/10.1109/4235.996017Google ScholarDigital Library
- Mark Harman and Phil McMinn. 2010. A theoretical and empirical study of search-based testing: Local, global, and hybrid search. IEEE Trans. Softw. Eng. 36, 2 (2010), 226--247. DOI:http://dx.doi.org/10.1109/TSE.2009.71Google ScholarDigital Library
- Abdullah Konak, David W. Coit, and Alice E. Smith. 2006. Multi-objective optimization using genetic algorithms: A tutorial. Reliabil. Eng. Syst. Safety 91, 9 (2006), 992--1007. DOI:http://dx.doi.org/10.1016/j.ress.2005.11.018Google ScholarCross Ref
- Kalyanmoy Deb and Himanshu Jain. 2014. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evolution. Comput. 18, 4 (2013), 577--601. DOI:http://dx.doi.org/10.1109/TEVC.2013.2281535Google ScholarCross Ref
- Himanshu Jain and Kalyanmoy Deb. 2014. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part Ii: Handling constraints and extending to an adaptive approach. IEEE Trans. Evolution. Comput. 18, 4 (2014), 602--622. DOI:http://dx.doi.org/10.1109/TEVC.2013.2281534Google ScholarCross Ref
- Indraneel Das and John E. Dennis. 1998. Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optimiz. 8, 3 (1998), 631--657. DOI:http://dx.doi.org/10.1137/S1052623496307510Google ScholarDigital Library
- Antonio J. Nebro, Juan J. Durillo, Francisco Luna, Bernabé Dorronsoro, and Enrique Alba, 2007. Design issues in a multiobjective cellular genetic algorithm. In Proceedings of the Conference on Evolutionary Multi-Criterion Optimization (EMO’07). Lecture Notes in Computer Science, Vol 4403, S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu, and T. Murata (Eds). Springer, Berlin, 126--140. DOI:http://dx.doi.org/10.1007/978-3-540-70928-2_13Google ScholarCross Ref
- Eckart Zitzler, Marco Laumanns, and Lothar Thiele. 2001. Spea2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. TIK-Report 103. Department of Electrical EngineeringSwiss Federal Institute of Technology (ETH) Zurich.Google Scholar
- Eckart Zitzler and Simon Künzli. 2004. Indicator-based selection in multiobjective search. In Proceedings of the Conference on Parallel Problem Solving from Nature (PPSN’04). Lecture Notes in Computer Science, Vol 3242, Xin Yao et al. (Eds). Springer, Berlin, 832--842. DOI:http://dx.doi.org/10.1007/978-3-540-30217-9_84Google ScholarCross Ref
- Joshua D. Knowles and David W. Corne. 1999. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Proceedings of the Congress on Evolutionary Computation (CEC’99). IEEE, 98--105. DOI:http://dx.doi.org/10.1109/CEC.1999.781913Google Scholar
- Shaukat Ali, Lionel C. Briand, Hadi Hemmati, and Rajwinder Kaur Panesar-Walawege. 2010. A systematic review of the application and empirical investigation of search-based test case generation. IEEE Trans. Softw. Eng. 36, 6 (2010), 742--762. DOI:http://dx.doi.org/10.1109/TSE.2009.52Google ScholarDigital Library
- Mark Harman and Bryan F. Jones. 2001. Search-based software engineering. Info. Softw. Technol. 43, 14 (2001), 833--839. DOI:http://dx.doi.org/10.1016/S0950-5849(01)00189-6Google ScholarCross Ref
- Joachim Karlsson. 1996. Software requirements prioritizing. In Proceedings of the 2nd International Conference on Requirements Engineering. IEEE, 110--116. DOI:http://dx.doi.org/10.1109/ICRE.1996.491435Google ScholarCross Ref
- ISO/IEC/IEEE. 2018. ISO/IEC/IEEE 29148:2018 Systems and software engineering – Life cycle processes – Requirements engineering.Google Scholar
- Yuanyuan Zhang, Mark Harman, and Soo Ling Lim. 2013. Empirical evaluation of search based requirements interaction management. Info. Softw. Technol. 55, 1 (2013), 126--152. DOI:http://dx.doi.org/10.1016/j.infsof.2012.03.007Google ScholarDigital Library
- Åsa G. Dahlstedt and Anne Persson. 2005. Requirements interdependencies: State of the art and future challenges. In Engineering and Managing Software Requirements, Springer, A. Aurum and C. Wohlin (Eds). 95—116. DOI:http://dx.doi.org/10.1007/3-540-28244-0_5Google Scholar
- Barry Boehm, Chris Abts, and Sunita Chulani. 2000. Software development cost estimation approaches—A survey. Ann. Softw. Eng. 10 (Nov. 2000), 177--205. DOI:http://dx.doi.org/10.1023/A:1018991717352Google Scholar
- Barry Boehm, Ray Madachy, and Bert Steece. 2000. Software cost estimation with Cocomo II with CDROM. Prentice Hall PTR, United States.Google ScholarDigital Library
- Olaf Helmer, Bernice Brown, and Theodore Gordon. 1966. Social Technology. Basic Books, New York, NY.Google Scholar
- Andrew R. Gray and Stephen G. MacDonell. 1997. A comparison of techniques for developing predictive models of software metrics. Info. Softw. Technol. 39, 6 (1997), 425--437. DOI:http://dx.doi.org/10.1016/S0950-5849(96)00006-7Google ScholarCross Ref
- Jay Wright Forrester. 1997. Industrial dynamics. J. Operation. Res. Soc. 48, 10 (1997), 1037--1041. DOI:http://dx.doi.org/10.1057/palgrave.jors.2600946Google ScholarCross Ref
- Sunita Chulani, Barry Boehm, and Bert Steece. 1999. Bayesian analysis of empirical software engineering cost models. IEEE Trans. Softw. Eng. 25, 4 (1999), 573--583. DOI:http://dx.doi.org/10.1109/32.799958Google ScholarDigital Library
- Abraham Charnes, William W. Cooper, and Edwardo Rhodes. 1978. Measuring the efficiency of decision making units. European J. Operation. Res. 2, 6 (1978), 429—444.Google ScholarCross Ref
- Chiang Kao and Shiang-Tai Liu. 2000. Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets Syst. 113, 3 (2000), 427--437. DOI:http://dx.doi.org/10.1016/S0165-0114(98)00137-7Google ScholarDigital Library
- William W. Cooper, Kyung Sam Park, and Gang Yu. 1999. Idea and ar-idea: Models for dealing with imprecise data in dea. Manage. Sci. 45, 4 (1999), 597--607. DOI:http://dx.doi.org/10.1287/mnsc.45.4.597Google Scholar
- Dennis Aigner, C. A. Knox Lovell, and Peter Schmidt. 1977. Formulation and estimation of stochastic frontier production function models. J. Econometr. 6, 1 (1977), 21--37.Google ScholarCross Ref
- Majid Azadi, Reza Farzipoor Saen, and Madjid Tavana. 2012. Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data. Int. J. Industr. Syst. Eng. 10, 2 (2012), 167--196. DOI:http://dx.doi.org/10.1504/IJISE.2012.045179Google ScholarCross Ref
- Scott E. Atkinson and Paul W. Wilson. 1995. Comparing mean efficiency and productivity scores from small samples: A bootstrap methodology. J. Product. Anal. 6, 2 (1995), 137--152. DOI:http://dx.doi.org/10.1007/BF01073408Google ScholarCross Ref
- Chiang Kao and Shiang-Tai Liu. 2009. Stochastic data envelopment analysis in measuring the efficiency of taiwan commercial banks. Eur. J. Operation. Res. 196, 1 (2009), 312--322. DOI:http://dx.doi.org/10.1016/j.ejor.2008.02.023Google ScholarCross Ref
- R. G. Dyson and Estelle A. Shale. 2010. Data envelopment analysis, operational research and uncertainty. J. Operation. Res. Soc. 61, 1 (2010), 25--34. DOI:http://dx.doi.org/10.1057/jors.2009.145Google ScholarCross Ref
- Magne Jørgensen and Kjetil Moløkken-Østvold. 2006. How large are software cost overruns? A review of the 1994 chaos report. Info. Softw. Technol. 48, 4 (2006), 297--301. DOI:http://dx.doi.org/10.1016/j.infsof.2005.07.002Google ScholarDigital Library
- Michael Bloch, Sven Blumberg, and Jürgen Laartz. 2012. Delivering large-scale it projects on time, on budget, and on value. In Harvard Business Review. 2--7.Google Scholar
- Boehm Barry. 1981. Software engineering economics. Prentice Hall, Upper Saddle River, NJ.Google Scholar
- Claes Wohlin, Per Runeson, Martin Höst, Magnus C. Ohlsson, Björn Regnell, and Anders Wesslén. 2012. Experimentation in Software Engineering. Springer Science 8 Business Media.Google ScholarCross Ref
- Barbara A. Kitchenham, Shari Lawrence Pfleeger, Lesley M. Pickard, Peter W. Jones, David C. Hoaglin, Khaled El Emam, and Jarrett Rosenberg. 2002. Preliminary guidelines for empirical research in software engineering. IEEE Trans. Softw. Eng. 28, 8 (2002), 721--734. DOI:http://dx.doi.org/10.1109/TSE.2002.1027796Google ScholarDigital Library
- Soo Ling Lim and Anthony Finkelstein. 2011. Stakerare: Using social networks and collaborative filtering for large-scale requirements elicitation. IEEE Trans. Softw. Eng. 38, 3 (2012), 707--735. DOI:http://dx.doi.org/10.1109/TSE.2011.36Google Scholar
- Shuai Wang, Shaukat Ali, Tao Yue, Yan Li, and Marius Liaaen. 2016. A practical guide to select quality indicators for assessing pareto-based search algorithms in search-based software engineering. In Proceedings of the 38th International Conference on Software Engineering. IEEE, 631--642. DOI:http://dx.doi.org/10.1145/2884781.2884880Google ScholarDigital Library
- Andrea Arcuri. 2013. It really does matter how you normalize the branch distance in search-based software testing. Softw. Test. Verificat. Reliabil. 23, 2 (2013), 119--147. DOI:http://dx.doi.org/10.1002/stvr.457Google ScholarCross Ref
- Barbara Kitchenham, Lech Madeyski, David Budgen, Jacky Keung, Pearl Brereton, Stuart Charters, Shirley Gibbs, and Amnart Pohthong. 2016. Robust statistical methods for empirical software engineering. Empir. Softw. Eng. 22 (2016), 579--630. DOI:http://dx.doi.org/10.1007/s10664-016-9437-5Google ScholarDigital Library
- Andrea Arcuri and Lionel Briand. 2011. A practical guide for using statistical tests to assess randomized algorithms in software engineering. In Proceedings of the 33rd International Conference on Software Engineering (ICSE’11). IEEE, 1-10. DOI:http://dx.doi.org/10.1145/1985793.1985795Google ScholarDigital Library
- R-Core-Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/.Google Scholar
- Indra M. Chakravarty, J. D. Roy, and Radha Govind Laha. 1967. Handbook of Methods of Applied Statistics. McGraw-Hill, New York, NY.Google Scholar
- William H. Kruskal and W. Allen Wallis. 1952. Use of ranks in one-criterion variance analysis. J. Amer. Stat. Assoc. 47, 260 (1952), 583--621. DOI:http://dx.doi.org/10.1080/01621459.1952.10483441Google ScholarCross Ref
- András Vargha and Harold D. Delaney. 2000. A critique and improvement of the cl common language effect size statistics of McGraw and Wong. J. Education. Behav. Stat. 25, 2 (2000), 101--132. DOI:http://dx.doi.org/10.3102/10769986025002101Google Scholar
- Olive Jean Dunn. 1964. Multiple comparisons using rank sums. Technometrics 6, 3 (1964), 241--252.Google ScholarCross Ref
- Carlo E. Bonferroni. 1936. Teoria statistica delle classi e calcolo delle probabilita. Libreria Internazionale Seeber.Google Scholar
- Maurice G. Kendall. 1938. A new measure of rank correlation. Biometrika 30, 1/2 (1938), 81--93. DOI:http://dx.doi.org/10.2307/2332226Google ScholarCross Ref
- Yuanyuan Zhang, Mark Harman, and S. Afshin Mansouri. 2007. The multi-objective next release problem. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. ACM, 1129--1137. DOI:http://dx.doi.org/10.1145/1276958.1277179Google Scholar
- Shin Yoo and Mark Harman. 2007. Pareto efficient multi-objective test case selection. In Proceedings of the International Symposium on Software Testing and Analysis. 140--150. DOI:http://dx.doi.org/10.1145/1273463.1273483Google ScholarDigital Library
- Aurora Ramirez, José Raúl Romero, and Sebastian Ventura. 2019. A survey of many-objective optimisation in search-based software engineering. J. Syst. Softw. 149 (2019), 382--395. DOI:http://dx.doi.org/10.1016/j.jss.2018.12.015Google ScholarCross Ref
- Wiem Mkaouer, Marouane Kessentini, Adnan Shaout, Patrice Koligheu, Slim Bechikh, Kalyanmoy Deb, and Ali Ouni. 2015. Many-objective software remodularization using nsga-iii. ACM Trans. Softw. Eng. Methodol. 24, 3 (2015), Article No. 17. DOI:http://dx.doi.org/10.1145/2729974Google ScholarDigital Library
- David E. Goldberg and Robert Lingle. 1985. Alleles, loci, and the traveling salesman problem. In Proceedings of the International Conference on Genetic Algorithms and Their Applications. Lawrence Erlbaum, Hillsdale, NJ, 154--159.Google Scholar
- Andrea Arcuri and Gordon Fraser. 2011. On parameter tuning in search based software engineering. In Proceedings of the International Symposium on Search Based Software Engineering (SSBSE’11). M. B. Cohen and M. Ó Cinnéide (Eds). Springer, Berlin, 33--47.Google ScholarDigital Library
- Kalyanmoy Deb and Ram Bhushan Agrawal. 1995. Simulated binary crossover for continuous search space. Technical Report IITK/ME/SMD-94027. Department of Mechanical Engineering, Indian Institute of Technology.Google Scholar
- Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao. 2015. Many-objective evolutionary algorithms: A survey. ACM Comput. Surveys 48, 1 (2015), Article No. 13. DOI:http://dx.doi.org/10.1145/2792984Google ScholarDigital Library
- Tom Gilb. 2005. Competitive engineering: A handbook for systems engineering, requirements engineering, and software engineering using planguage. Butterworth-Heinemann, Newton, MA.Google Scholar
- Pär Carlshamre, Kristian Sandahl, Mikael Lindvall, Björn Regnell, and Och Dag J. Nattz. 2001. An industrial survey of requirements interdependencies in software product release planning. In Proceedings of the International Symposium on Requirements Engineering. IEEE, 84--91. DOI:http://dx.doi.org/10.1109/ISRE.2001.948547Google ScholarCross Ref
- Klaus Pohl. 1996. Process-centered requirements engineering. John Wiley 8 Sons, Inc., New York, NY.Google Scholar
- Johan Dag, Björn Regnell, Pär Carlshamre, Michael Andersson, and Joachim Karlsson. 2002. A feasibility study of automated natural language requirements analysis in market-driven development. Require. Eng. 7, 1 (2002), 20--33. DOI:http://dx.doi.org/10.1007/s007660200002Google Scholar
- Faiza Allah Bukhsh, Zaharah Allah Bukhsh, and Maya Daneva. 2020. A systematic literature review on requirement prioritization techniques and their empirical evaluation. Comput. Standards Interfaces 69 (2020), 103389. DOI:http://dx.doi.org/10.1016/j.csi.2019.103389Google ScholarCross Ref
- Aimin Zhou, BoYang Qu, Hui Li, ShiZheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. 2011. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm Evolution. Comput. 1, 1 (2011), 32--49. DOI:http://dx.doi.org/10.1016/j.swevo.2011.03.001Google ScholarCross Ref
- Mark Harman. 2010. Why the virtual nature of software makes it ideal for search based optimization. In Fundamental Approaches to Software Engineering, David S. Rosenblum and Gabriele Taentzer (Eds). Springer, Berlin, 1--12. DOI:http://dx.doi.org/10.1007/978-3-642-12029-9_1Google Scholar
- Anthony J. Bagnall, Victor J. Rayward-Smith, and Ian M. Whittley. 2001. The next release problem. Info. Softw. Technol. 43, 14 (2001), 883--890. DOI:http://dx.doi.org/10.1016/S0950-5849(01)00194-XGoogle ScholarCross Ref
- E. Robert Bixby, Mary Fenelon, Zonghao Gu, Ed Rothberg, and Roland Wunderling, 1999. Mip: Theory and practice—closing the gap. In Proceedings of the Conference on System Modeling and Optimization (CSMO’99). Springer, M. J. D. Powell and S. Scholtes S. (Eds). 19--49. DOI:http://dx.doi.org/10.1007/978-0-387-35514-6_2Google Scholar
- Thomas A. Feo and Mauricio G. C. Resende. 1995. Greedy randomized adaptive search procedures. J. Global Optimiz. 6, 2 (1994), 109--134.Google ScholarCross Ref
- Moshood Omolade Saliu and Guenther Ruhe. 2007. Bi-objective release planning for evolving software systems. In Proceedings of the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering. ACM, 105--114. DOI:http://dx.doi.org/10.1145/1287624.1287641Google ScholarDigital Library
- Y. V. Haimes. 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Trans. Syst. Man Cybernet. 1, 3 (1971), 296--297. DOI:http://dx.doi.org/10.1109/TSMC.1971.4308298Google Scholar
- A. Charan Kumari, K. Srinivas, and M. P. Gupta. 2012. Software requirements selection using quantum-inspired elitist multi-objective evolutionary algorithm. In Proceedings of the International Conference on Advances in Engineering, Science and Management (ICAESM’12). IEEE, 782--787. DOI:http://dx.doi.org/10.1109/CONSEG.2012.6349487Google Scholar
- Des Greer and Guenther Ruhe. 2004. Software release planning: an evolutionary and iterative approach. Info. Softw. Technol. 46, 4 (2004), 243--253. DOI:http://dx.doi.org/10.1016/j.infsof.2003.07.002Google ScholarCross Ref
- Chen Li, Marjan Van denAkker, Sjaak Brinkkemper, and Guido Diepen. 2010. An integrated approach for requirement selection and scheduling in software release planning. Require. Eng. 15, 4 (2010), 375--396. DOI:http://dx.doi.org/10.1007/s00766-010-0104-xGoogle ScholarDigital Library
- Juan Li, Liming Zhu, Ross Jeffery, Yan Liu, He Zhang, Qing Wang, and Mingshu Li. 2012. An initial evaluation of requirements dependency types in change propagation analysis. In Proceedings of the 16th International Conference on Evaluation 8 Assessment in Software Engineering (EASE’12). IET, 62--71. DOI:http://dx.doi.org/10.1049/ic.2012.0009Google Scholar
- Paolo Tonella, Angelo Susi, and Francis Palma. 2013. Interactive requirements prioritization using a genetic algorithm. Info. Softw. Technol. 55, 1 (2013), 173--187. DOI:http://dx.doi.org/10.1016/j.infsof.2012.07.003Google ScholarDigital Library
- Huihui Zhang, Shuai Wang, Tao Yue, Shaukat Ali, and Chao Liu. 2017. Search and similarity based selection of use case scenarios: An empirical study. Empir. Softw. Eng. 23, 1 (2017), 87--164. DOI:http://dx.doi.org/10.1007/s10664-017-9500-xGoogle ScholarDigital Library
- Muhammad Aasem, Muhammad Ramzan, and Arfan Jaffar. 2010. Analysis and optimization of software requirements prioritization techniques. In Proceedings of the International Conference on Information and Emerging Technologies (ICIET’10). IEEE, 1--6. DOI:http://dx.doi.org/10.1109/ICIET.2010.5625687Google ScholarCross Ref
- Paula Laurent, Jane Cleland-Huang, and Chuan Duan. 2007. Towards automated requirements triage. In Proceedings of the 15th IEEE International Requirements Engineering Conference. IEEE, 131--140. DOI:http://dx.doi.org/10.1109/RE.2007.63Google ScholarCross Ref
- Claes Wohlin and Aybüke Aurum. 2005. Engineering and Managing Software Requirements. Springer Science 8 Business Media, Berlin.Google Scholar
- Paolo Avesani, Cinzia Bazzanella, Anna Perini, and Angelo Susi. 2005. Facing scalability issues in requirements prioritization with machine learning techniques. In Proceedings of the 13th IEEE International Conference on Requirements Engineering (RE'05). IEEE, 297--305. DOI:http://dx.doi.org/10.1109/RE.2005.30Google ScholarDigital Library
- Gerald Kotonya and Ian Sommerville. 1998. Requirements engineering: Processes and techniques. John Wiley 8 Sons, Chichester.Google ScholarDigital Library
- Persis Voola and A. Vinaya Babu. 2012. Requirements Uncertainty Prioritization Approach: A Novel Approach for Requirements Prioritization. Software Engineering: An International Journal (SEIJ) 2, No. 2 (2012), 37--49.Google Scholar
- Roseanna W. Saaty. 1987. The analytic hierarchy process—What it is and how it is used. Math. Model. 9, 3–5 (1987), 161--176. DOI:http://dx.doi.org/10.1016/0270-0255(87)90473-8Google ScholarCross Ref
- Vittorio Cortellessa, Ivica Crnkovic, Fabrizio Marinelli, and Pasqualina Potena. 2008. Experimenting the automated selection of cots components based on cost and system requirements. J. Univers. Comput. Sci. 14, 8 (2008), 1228--1255.Google Scholar
- Dean Leffingwell and Don Widrig. 2000. Managing Software Requirements: A Unified Approach. Addison-Wesley Professional, Boston.Google ScholarDigital Library
- Joachim Karlsson, Claes Wohlin, and Björn Regnell. 1998. An evaluation of methods for prioritizing software requirements. Info. Softw. Technol. 39, 14–15, 939--947. DOI:http://dx.doi.org/10.1016/S0950-5849(97)00053-0Google ScholarCross Ref
- Raja Masadeh, Amjad Hudaib, and Abdullah Alzaqebah. 2018. Wgw: A hybrid approach based on whale and grey wolf optimization algorithms for requirements prioritization. Adv. Syst. Sci. Appl. 18, 2 (2018), 63--83. DOI:http://dx.doi.org/10.25728/assa.2018.18.2.576Google Scholar
- Seyedali Mirjalili and Andrew Lewis. 2016. The whale optimization algorithm. Adv. Eng. Softw. 95 (2016), 51--67. DOI:http://dx.doi.org/10.1016/j.advengsoft.2016.01.008Google ScholarDigital Library
- Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis. 2014. Grey wolf optimizer. Adv. Eng. Softw. 69 (2014), 46--61. DOI:http://dx.doi.org/10.1016/j.advengsoft.2013.12.007Google ScholarDigital Library
- Abdullah Alzaqebah, Raja Masadeh, and Amjad Hudaib. 2018. Whale optimization algorithm for requirements prioritization. In Proceedings of the 9th International Conference on Information and Communication Systems (ICICS’18). IEEE, 84--89. DOI:http://dx.doi.org/10.1109/IACS.2018.8355446Google ScholarCross Ref
- R. Vijay Anand and M. Dinakaran. 2018. Whalerank: An optimisation based ranking approach for software requirements prioritisation. International J. Environ. Waste Manage. 21, 1 (2018), 1--21. DOI:http://dx.doi.org/10.1504/IJEWM.2018.091307Google ScholarCross Ref
- Ankita Gupta and Chetna Gupta. 2018. CDBR: A semi-automated collaborative execute-before-after dependency-based requirement prioritization approach. J. King Saud University-Comput. Info. Sci. Available online 7 October 2018 (Article In Process). DOI:https://doi.org/10.1016/j.jksuci.2018.10.004Google Scholar
- Heena Ahuja and Usha Batra, 2018. Performance enhancement in requirement prioritization by using least-squares-based random genetic algorithm. In Innovations in Computational Intelligence, Springer, Singapore, B. Panda, S. Sharma, and U. Batra (Eds). 251--263. DOI:http://dx.doi.org/10.1007/978-981-10-4555-4_17Google Scholar
- Man Zhang, Tao Yue, Shaukat Ali, Bran Selic, Oscar Okariz, Roland Norgre, and Karmele Intxausti. 2018. Specifying uncertainty in use case models. J. Syst. Softw. 144 (2018), 573--603. DOI:http://dx.doi.org/10.1016/j.jss.2018.06.075Google ScholarDigital Library
- Betty H. C. Cheng, Pete Sawyer, Nelly Bencomo, and Jon Whittle. 2009. A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty. In Proceedings of the International Conference on Model Driven Engineering Languages and Systems. Springer, Berlin, 468--483. DOI:http://dx.doi.org/10.1007/978-3-642-04425-0_36Google ScholarDigital Library
- Jon Whittle, Pete Sawyer, Nelly Bencomo, Betty H. C. Cheng, and Jean-Michel Bruel. 2010. Relax: A language to address uncertainty in self-adaptive systems requirement. Require. Eng. 15, 2 (2010), 177--196. DOI:http://dx.doi.org/10.1007/s00766-010-0101-0Google ScholarDigital Library
- Pete Sawyer, Nelly Bencomo, Jon Whittle, Emmanuel Letier, and Anthony Finkelstein. 2010. Requirements-aware systems: A research agenda for re for self-adaptive systems. In Proceedings of the 18th IEEE International Requirements Engineering Conference. IEEE, 95--103. DOI:http://dx.doi.org/10.1109/RE.2010.21Google ScholarDigital Library
- Rick Salay, Marsha Chechik, Jennifer Horkoff, and Alessio Di Sandro. 2013. Managing requirements uncertainty with partial models. Require. Eng. 18, 2 (2013), 107--128. DOI:http://dx.doi.org/10.1007/s00766-013-0170-yGoogle ScholarDigital Library
- Rick Salay, Michalis Famelis, and Marsha Chechik. 2012. Language independent refinement using partial modeling. In Fundamental Approaches to Software Engineering. Fase 2012. Lecture Notes in Computer Science, Vol 7212, J. de Lara and A. Zisman (Eds). Springer, Berlin, 224--239. DOI:http://dx.doi.org/10.1007/978-3-642-28872-2_16Google ScholarDigital Library
- Michalis Famelis and Stephanie Santosa. 2013. Mav-Vis: A notation for model uncertainty. In Proceedings of the 5th International Workshop on Modeling in Software Engineering (MiSE’13). IEEE, 7--12. DOI:http://dx.doi.org/10.1109/MiSE.2013.6595289Google ScholarCross Ref
- Naeem Esfahani and Sam Malek. 2013. Uncertainty in self-adaptive software systems. In Software Engineering for Self-Adaptive Systems II, R. de Lemos, H. Giese, H. A. Müller. and M. Shaw (Eds). Springer, Berlin, 214--238. DOI:http://dx.doi.org/10.1007/978-3-642-35813-5_9Google Scholar
- Tao Yue, Lionel C. Briand, and Yvan Labiche. 2015. aToucan: An automated framework to derive uml analysis models from use case models. ACM Trans. Softw. Eng. Methodol. 24, 3 (2015), Article No.: 13. DOI:http://dx.doi.org/10.1145/2699697Google ScholarDigital Library
- Tao Yue, Lionel C. Briand, and Yvan Labiche. 2013. Facilitating the transition from use case models to analysis models: Approach and experiments. ACM Trans. Softw. Eng. Methodol. 22, 1 (2013), Article No.: 5 DOI:http://dx.doi.org/10.1145/2430536.2430539Google ScholarDigital Library
- Man Zhang, Bran Selic, Shaukat Ali, Tao Yue, Oscar Okariz, and Roland Norgren, 2016. Understanding uncertainty in cyber-physical systems: A conceptual model. In Proceedings of the Modelling Foundations and Applications (ECMFA’16). Lecture Notes in Computer Science, Vol 9764, A. Wąsowski and H. Lönn H. (Eds). Springer, Cham, 247--264. DOI:http://dx.doi.org/10.1007/978-3-319-42061-5_16Google Scholar
- Ahmed Al-Emran, Puneet Kapur, Dietmar Pfahl, and Guenther Ruhe. 2010. Studying the impact of uncertainty in operational release planning—An integrated method and its initial evaluation. Info. Softw. Technol. 52, 4 (2010), 446--461. DOI:http://dx.doi.org/10.1016/j.infsof.2009.11.003Google ScholarDigital Library
Index Terms
- Uncertainty-wise Requirements Prioritization with Search
Recommendations
Requirements Prioritization Based on Benefit and Cost Prediction: A Method Classification Framework
SEAA '08: Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced ApplicationsIn early phases of the software development process, requirements prioritization necessarily relies on the specified requirements and on predictions of benefit and cost of individual requirements. This paper induces a conceptual model of requirements ...
Prioritization of quality requirements: State of practice in eleven companies
RE '11: Proceedings of the 2011 IEEE 19th International Requirements Engineering ConferenceRequirements prioritization is recognized as an important but challenging activity in software product development. For a product to be successful, it is crucial to find the right balance among competing quality requirements. Although literature offers ...
Adaptive Requirements Prioritization ARP: Improving Decisions between Conflicting Requirements
Prioritization of requirements is a core activity of requirements engineering. Conventionally used to resolve conflicting requirements, it can be performed on a wide variety of attributes, reflecting, for example, stakeholder value, value to business, ...
Comments