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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects

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Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14125))

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Abstract

Software development project requires proper planning to mitigate risk and uncertainty. Overtime planning within software project management has been receiving attention recently from search-based software engineering researchers. Multi-objective evolutionary algorithms are used to build automated tools that could effectively help Project Managers (PM) plan overtime on project schedules. Existing models however lack applicability by the PMs due to their disregard for expert knowledge in planning overtime. This study proposes a new interactive problem formulation for software overtime planning and presents a framework for building a machine learning-based interactive multi-objective optimization algorithm for overtime planning in software development projects. The framework is designed to train a priori a machine learning model to mimic the PM’s subjective judgment of overtime plans within the project schedule. The machine learning model is integrated with a memetic multi-objective optimization algorithm via an interactive module. Also, the memetic algorithm incorporates a preference-based w-dominance method for selecting non-dominated solutions. The proposed framework will be developed to assist software project managers to better plan overtime in order to prevent the expected risk of software development overrun.

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References

  1. Ferrucci, F., Harman, M., Sarro, F.: Search-based software project management. In: Ruhe, G., Wohlin, C. (eds.) Software Project Management in a Changing World, pp. 373–399. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55035-5_15

    Chapter  Google Scholar 

  2. Kuutila, M., Mäntylä, M., Farooq, U., Claes, M.: Time pressure in software engineering: a systematic review (2020). https://doi.org/10.1016/j.infsof.2020.106257

  3. Moløkken, K., Jørgensen, M.: A review of surveys on software effort estimation. In: International Symposium on Empirical Software Engineering, ISESE 2003, pp. 223–230. IEEE (2003)

    Google Scholar 

  4. Alba, E., Francisco Chicano, J.: Software project management with GAs. Inf. Sci. (NY) 177, 2380–2401 (2007). https://doi.org/10.1016/j.ins.2006.12.020

    Article  Google Scholar 

  5. Crawford, B., Soto, R., Johnson, F., Monfroy, E., Paredes, F.: A max-min ant system algorithm to solve the software project scheduling problem. Expert Syst. Appl. 41, 6634–6645 (2014). https://doi.org/10.1016/j.eswa.2014.05.003

    Article  Google Scholar 

  6. Luna, F., González-Álvarez, D.L., Chicano, F., Vega-Rodríguez, M.A.: The software project scheduling problem: a scalability analysis of multi-objective metaheuristics. Appl. Soft Comput. J. 15, 136–148 (2014). https://doi.org/10.1016/j.asoc.2013.10.015

    Article  Google Scholar 

  7. Oladele, R.O., Mojeed, H.A.: A shuffled frog-leaping algorithm for optimal software project planning. Afr. J. Comput. ICT. 7, 147–152 (2014)

    Google Scholar 

  8. Rachman, V., Ma’sum, A.M.: Comparative analysis of ant colony extended and mix min ant system in SW project scheduling problem. In: Proceedings - WBIS 2017 2017 International Workshop on Big Data and Information Security, vol. 8, pp. 85–91 (2017)

    Google Scholar 

  9. Ferrucci, F., Harman, M., Ren, J., Sarro, F.: Not going to take this anymore: multi-objective overtime planning for software engineering projects. In: Proceedings - International Conference on Software Engineering, pp. 462–471 (2013). https://doi.org/10.1109/ICSE.2013.6606592

  10. Akula, B., Cusick, J.: Impact of overtime and stress on software quality. In: WMSCI 2008 - The 12th World Multi-Conference on Systemics, Cybernetics, and Informatics, Jointly with the 14th International Conference on Information Systems Analysis and Synthesis, ISAS 2008 - Proceedings, p. 214 (2008). https://doi.org/10.13140/RG.2.2.12815.59041

  11. Kleppa, E., Sanne, B., Tell, G.S.: Working overtime is associated with anxiety and depression: the Hordaland health study. J. Occup. Environ. Med. 50, 658–666 (2008). https://doi.org/10.1097/JOM.0b013e3181734330

    Article  Google Scholar 

  12. Claes, M., Mäntylä, M., Kuutila, M., Adams, B.: Abnormal working hours: effect of rapid releases and implications to work content. In: 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR), pp. 243–247 (2017). https://doi.org/10.1109/MSR.2017.3

  13. Kuutila, M., Mäntylä, M.V., Claes, M., Elovainio, M.: Daily questionnaire to assess self-reported well-being during a software development project. In: 2018 IEEE/ACM 3rd International Workshop on Emotion Awareness in Software Engineering (SEmotion), pp. 39–43 (2018)

    Google Scholar 

  14. Van Der Hulst, M., Geurts, S.: Associations between overtime and psychological health in high and low reward jobs. Work Stress. 15, 227–240 (2001). https://doi.org/10.1080/026783701110.1080/02678370110066580

    Article  Google Scholar 

  15. Hajjdiab, H., Taleb, A.S.: Adopting agile software development: issues and challenges. Int. J. Manag. Value Supply Chain. 2, 1–10 (2011). https://doi.org/10.5121/ijmvsc.2011.2301

    Article  Google Scholar 

  16. Capodieci, A., Mainetti, L., Manco, L.: A case study to enable and monitor real IT companies migrating from waterfall to agile. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8583, pp. 119–134. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09156-3_9

    Chapter  Google Scholar 

  17. Alashqur, A.: Towards a broader adoption of agile software development methods. Int. J. Adv. Comput. Sci. Appl. 7, 94–98 (2016). https://doi.org/10.14569/ijacsa.2016.071212

  18. Faisal Abrar, M., et al.: De-motivators for the adoption of agile methodologies for large-scale software development teams: an SLR from management perspective (2020). https://doi.org/10.1002/smr.2268

    Article  Google Scholar 

  19. Ali, S., Hongqi, L., Abrar, M.F.: Systematic literature review of critical barriers to software outsourcing partnership. In: 2018 5th International Multi-Topic ICT Conference (IMTIC), pp. 1–8 (2018). https://doi.org/10.1109/IMTIC.2018.8467254

  20. DeO Barros, M., De Araujo, L.A.O.: Learning overtime dynamics through multiobjective optimization. In: GECCO 2016 - Proceedings 2016 Genetic and Evolutionary Computation Conference, pp. 1061–1068 (2016). https://doi.org/10.1145/2908812.2908824

  21. Sarro, F., Ferrucci, F., Harman, M., Manna, A., Ren, J.: Adaptive multi-objective evolutionary algorithms for overtime planning in software projects. IEEE Trans. Softw. Eng. 43, 898–917 (2017). https://doi.org/10.1109/TSE.2017.2650914

    Article  Google Scholar 

  22. Mojeed, H.A., Bajeh, A.O., Balogun, A.O., Adeleke, H.O.: Memetic approach for multi-objective overtime planning in software engineering projects. J. Eng. Sci. Technol. 14, 3213–3233 (2019)

    Google Scholar 

  23. Saraiva, R., Araújo, A.A., Dantas, A., Yeltsin, I., Souza, J.: Incorporating decision maker’s preferences in a multi-objective approach for the software release planning. J. Braz. Comput. Soc. 23 (2017). https://doi.org/10.1186/s13173-017-0060-0

  24. Simons, C.L., Smith, J., White, P.: Interactive ant colony optimization (iACO) for early lifecycle software design. Swarm Intell. 8, 139–157 (2014). https://doi.org/10.1007/s11721-014-0094-2

    Article  Google Scholar 

  25. Tonella, P., Susi, A., Palma, F.: Interactive requirements prioritization using a genetic algorithm. Inf. Softw. Technol., 173–187 (2013). https://doi.org/10.1016/j.infsof.2012.07.003

  26. Wang, T., Zhou, M.: A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction. Int. J. Ind. Ergon. 76, 102901 (2020). https://doi.org/10.1016/j.ergon.2019.102901

    Article  Google Scholar 

  27. Bavota, G., Carnevale, F., De Lucia, A., Di Penta, M., Oliveto, R.: Putting the developer in-the-loop: an interactive GA for software re-modularization. In: Fraser, G., Teixeira de Souza, J. (eds.) SSBSE 2012. LNCS, vol. 7515, pp. 75–89. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33119-0_7

    Chapter  Google Scholar 

  28. Dantas, A., Yeltsin, I., Araújo, A.A., Souza, J.: Interactive software release planning with preferences base. In: Barros, M., Labiche, Y. (eds.) SSBSE 2015. LNCS, vol. 9275, pp. 341–346. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22183-0_32

    Chapter  Google Scholar 

  29. Nair, V., et al.: Data-driven search-based software engineering. In: Proceedings - International Conference on Software Engineering, pp. 341–352 (2018). https://doi.org/10.1145/3196398.3196442

  30. Shafiq, S., Mashkoor, A., Mayr-Dorn, C., Egyed, A.: Machine learning for software engineering: a systematic mapping. arXiv Prepr. arXiv:2005.13299 (2020)

  31. Shafiq, S., Mashkoor, A., Mayr-Dorn, C., Egyed, A.: A literature review of using machine learning in software development life cycle stages. IEEE Access 9, 140896–140920 (2021). https://doi.org/10.1109/ACCESS.2021.3119746

    Article  Google Scholar 

  32. Marculescu, B., Poulding, S., Feldt, R., Petersen, K., Torkar, R.: Tester interactivity makes a difference in search-based software testing: a controlled experiment. Inf. Softw. Technol. 78, 66–82 (2016). https://doi.org/10.1016/j.infsof.2016.05.009

    Article  Google Scholar 

  33. Ghannem, A., El Boussaidi, G., Kessentini, M.: Model refactoring using interactive genetic algorithm. In: Ruhe, G., Zhang, Y. (eds.) SSBSE 2013. LNCS, vol. 8084, pp. 96–110. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39742-4_9

    Chapter  Google Scholar 

  34. Szlapczynski, R., Szlapczynska, J.: W-dominance: tradeoff-inspired dominance relation for preference-based evolutionary multi-objective optimization. Swarm Evol. Comput. 63, 100866 (2021). https://doi.org/10.1016/j.swevo.2021.100866

    Article  Google Scholar 

  35. Szlapczynska, J., Szlapczynski, R.: Preference-based evolutionary multi-objective optimization in ship weather routing. Appl. Soft Comput. J. 84, 105742 (2019). https://doi.org/10.1016/j.asoc.2019.105742

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Applied Computer Science, Institute of Ocean Engineering and Ship Technology, Gdansk University of Technology, Gdansk, Poland

    Hammed A. Mojeed & Rafal Szlapczynski

Authors
  1. Hammed A. Mojeed
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  2. Rafal Szlapczynski
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Correspondence to Hammed A. Mojeed .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Mojeed, H.A., Szlapczynski, R. (2023). Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_35

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  • DOI: https://doi.org/10.1007/978-3-031-42505-9_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42504-2

  • Online ISBN: 978-3-031-42505-9

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