Incorporation of ISO 25010 with machine learning to develop a novel quality in use prediction system (QiUPS)

  • Osama Alshareet
  • Awni Itradat
  • Iyad Abu Doush
  • Ahmad Quttoum
Original Article


Guided by the eagerness to fulfill business objectives, quality assurance has become one of the highlighted topics in software engineering. With the rise of globalization and free markets, software users are becoming increasingly powerful with their ability to buy or reject computer software. While there is agreement over achieving quality, there is debate over the definition of quality. To illustrate, literature shows inconsistencies between a software development team definition to quality and a user definition to quality. Recently, there is a tendency amongst researchers to appreciate the need for studying quality from a user prospective. Following a systematic approach, this research attempts to develop a QiUPS, an expert system for predicting quality in use in early software development phases. With the scariness of research data in this field, the research generates a dataset from the documentation of Information, Communication, and E-learning Technology Centre software projects. The research methodology followed a comparative approach as it statistically compared four different classification algorithms (CAs) in terms of accuracy in classifying the research dataset. After that, the research results led the researchers to compare the performance of artificial neural networks with convolutional neural networks in three empirical experiments, which is rarely researched. Finally, the research incorporated the best CA with ISO 25010 in order to develop the novel QiUPS. The research results are consistent and contributive to this rarely researched area.


Quality in use prediction system (QiUPS) ISO 25010 software quality model Classification algorithms (CAs) Artificial neural networks (ANN) Convolutional neural networks (CNN) Quality in use (QiU) User-centered applications (UCA) Multi-layer perceptron (MLP) 


  1. Abe S et al (2006) Estimation of project success using bayesian classifier. In: Proceedings of the 28th international conference on software engineering. ACM, pp 600–603Google Scholar
  2. Ahimbisibwe A, Cavana RY, Daellenbach U (2015) A contingency fit model of critical success factors for software development projects: a comparison of agile and traditional plan-based methodologies. J Enterp Inf Manag 28(1):7–33CrossRefGoogle Scholar
  3. Alnanih R, Ormandjieva O, Radhakrishnan T (2012) A new methodology (CON-INFO) for context-based development of a mobile user interface in healthcare applications. In: Pervasive health. Springer, London, pp 317–342Google Scholar
  4. Ardito C, Lanzilotti R, Sikorski M, Garnik I (2014) Can evaluation patterns enable end users to evaluate the quality of an e-learning system? An exploratory study. In: Universal access in human–computer interaction. Universal access to information and knowledge. Springer, New York, pp 185–196Google Scholar
  5. Becker P, Lew P, Olsina L (2012) Specifying process views for a measurement, evaluation, and improvement strategy. Adv Softw Eng. doi: 10.1155/2012/949746 Google Scholar
  6. Bevan N (2009) Extending quality in use to provide a framework for usability measurement. In: Kurosu M (ed) HCD 2009, LNCS, vol 5619. Springer, Heidelberg, pp 13–22Google Scholar
  7. Burr GW (2015) Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses) using phase-change memory as the synaptic weight element. IEEE Trans Electron Dev 62(11):3498–3507CrossRefGoogle Scholar
  8. Cerpa N, Bardeen MD, Kitchenham B, Verner JM (2010) Evaluating logistic regression models to estimate software project outcomes. Inf Softw Technol 52(9):934–944CrossRefGoogle Scholar
  9. Cheng M, Wu Y (2008) Dynamic prediction of project success using evolutionary support vector machine inference model. In: Proceedings of the 25th international symposium on automation and robotics in constructionGoogle Scholar
  10. Craven MW, Shavlik JW (2014) Learning symbolic rules using artificial neural networks. In: Proceedings of the tenth international conference on machine learning, pp 73–80Google Scholar
  11. Deming WE (2000) Out of the crisis. MIT Press, CambridgeGoogle Scholar
  12. Dwivedi YK et al (2015) Research on information systems failures and successes: status update and future directions. Inf Syst Front 17(1):143–157CrossRefGoogle Scholar
  13. El Emam K, Koru AG (2008) A replicated survey of IT software project failures. IEEE Softw 25(5):84–90CrossRefGoogle Scholar
  14. El Halees AM (2014) Software usability evaluation using opinion mining. J Softw 9(2):343–349Google Scholar
  15. Gefen D, Straub D (2001) The relative importance of perceived ease-of-use in IS adoption: a study of e-commerce adoption. JAIS 1:1CrossRefGoogle Scholar
  16. González JL, García R, Brunetti JM, Gil R, Gimeno JM (2012) SWET-QUM: a quality in use extension model for semantic web exploration tools. In: Proceedings of the 13th international conference on Interacción Persona-Ordenador. ACM, New York, pp 15:1–15:8Google Scholar
  17. Heinrich R (2014) Business process quality. In: Aligning business processes and information systems, vol 22. Springer Fachmedien Wiesbaden, WiesbadenGoogle Scholar
  18. Heravi A, Coffey V, Trigunarsyah B (2015) Evaluating the level of stakeholder involvement during the project planning processes of building projects. Int J Project Manag 33(5):985–997CrossRefGoogle Scholar
  19. Hoffman T (1999) Study: 85% of IT departments fail to meet business needs. Computerworld 33:24Google Scholar
  20. Hussain A, Mkpojiogu EO (2016) Requirements: towards an understanding on why software projects fail. In: AIP conference proceedings. AIP Publishing LLCGoogle Scholar
  21. International Organization for Standardization (2011) ISO/IEC 25010:2011. Accessed 5 Jan 2017
  22. Jan SR et al (2016) Issues in global software development (communication, coordination and trust)—a critical review. Training 6(7):8Google Scholar
  23. Jørgensen M, Moløkken-Østvold K (2006) How large are software cost overruns? A review of the 1994 CHAOS report. Inf Softw Technol 48:297–301CrossRefGoogle Scholar
  24. Kim J, Jeong DH, Lee D, Jung H (2015) User-centered innovative technology analysis and prediction application in mobile environment. Multimed Tools Appl 74(20):8761–8779CrossRefGoogle Scholar
  25. La HHJ, Kim SDS (2013) A model of quality-in-use for service-based mobile ecosystem. In: 2013 1st international workshop on the engineering of mobile-enabled systems (MOBS). IEEE, New York, pp 13–18Google Scholar
  26. Lippert SK, Govindarajulu C (2015) Technological, organizational, and environmental antecedents to web services adoption. Commun IIMA 6(1):14Google Scholar
  27. Liu B, Lin J, Sadeh N (2014) Reconciling mobile app privacy and usability on smartphones: could user privacy profiles help? In: Proceedings of the 23rd international conference on world wide web. ACM, pp 201–212Google Scholar
  28. Mizuno O, Hamasaki T, Takagi Y, Kikuno T (2004) An empirical evaluation of predicting runaway software projects using Bayesian classification. Springer, BerlinCrossRefGoogle Scholar
  29. Oliveira J, Tereso A, Machado RJ (2014) An application to select collaborative project management software tools. New perspectives in information systems and technologies, vol 1. Springer, New York, pp 467–476CrossRefGoogle Scholar
  30. Orehovački T, Granić A, Kermek D (2013) Evaluating the perceived and estimated quality in use of Web 2.0 applications. J Syst Softw 86(12):3039–3059CrossRefGoogle Scholar
  31. Osman NB, Osman IM (2013) Attributes for the quality in use of mobile government systems. In: 2013 International conference on computing, electrical and electronics engineering (ICCEEE), pp 274–279Google Scholar
  32. Oztekin A et al (2013) A machine learning-based usability evaluation method for e-learning systems. Decis Support Syst 56:63–73CrossRefGoogle Scholar
  33. Reyes F, Cerpa N, Candia-Véjar A, Bardeen MD (2011) The optimization of success probability for software projects using genetic algorithms. J Syst Soft 84(5):775–785CrossRefGoogle Scholar
  34. Sainath TN et al (2015) Deep convolutional neural networks for large-scale speech tasks. Neural Netw 64:39–48CrossRefGoogle Scholar
  35. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  36. Smite D (2007) Project outcome predictions: risk barometer based on historical data. In: International conference on global software engineering (ICGSE 2007), pp 103–112Google Scholar
  37. Verner JM, Evanco WM, Cerpa N (2007) State of the practice: how important is effort estimation to software development success? Inf Softw Technol 49:181–193CrossRefGoogle Scholar
  38. Wang Y (2007) Prediction of success in open source software development. Master, University of CaliforniaGoogle Scholar
  39. Woodroof J, Kasper GM (1998) A conceptual development of process and outcome user satisfaction. In: Garrity EJ, Saunders GL (eds) Information system success measurement. Idea Publishing Group, Hershey, pp 122–132Google Scholar

Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017

Authors and Affiliations

  • Osama Alshareet
    • 1
  • Awni Itradat
    • 2
  • Iyad Abu Doush
    • 3
  • Ahmad Quttoum
    • 2
  1. 1.ICETHashemite UniversityZarqaJordan
  2. 2.Department of Computer EngineeringHashemite UniversityZarqaJordan
  3. 3.Department of Computer SciencesYarmouk UniversityIrbidJordan

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