Skip to main content

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


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  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 600–603

  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):7–33

    Article  Google 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 317–342

  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 185–196

  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 13–22

    Google 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):3498–3507

    Article  Google 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):934–944

    Article  Google 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 construction

  10. Craven MW, Shavlik JW (2014) Learning symbolic rules using artificial neural networks. In: Proceedings of the tenth international conference on machine learning, pp 73–80

  11. Deming WE (2000) Out of the crisis. MIT Press, Cambridge

    Google Scholar 

  12. Dwivedi YK et al (2015) Research on information systems failures and successes: status update and future directions. Inf Syst Front 17(1):143–157

    Article  Google Scholar 

  13. El Emam K, Koru AG (2008) A replicated survey of IT software project failures. IEEE Softw 25(5):84–90

    Article  Google Scholar 

  14. El Halees AM (2014) Software usability evaluation using opinion mining. J Softw 9(2):343–349

    Google 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 1:1

    Article  Google 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 15:1–15:8

  17. Heinrich R (2014) Business process quality. In: Aligning business processes and information systems, vol 22. Springer Fachmedien Wiesbaden, Wiesbaden

  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):985–997

    Article  Google Scholar 

  19. Hoffman T (1999) Study: 85% of IT departments fail to meet business needs. Computerworld 33:24

    Google Scholar 

  20. Hussain A, Mkpojiogu EO (2016) Requirements: towards an understanding on why software projects fail. In: AIP conference proceedings. AIP Publishing LLC

  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):8

    Google Scholar 

  23. Jørgensen M, Moløkken-Østvold K (2006) How large are software cost overruns? A review of the 1994 CHAOS report. Inf Softw Technol 48:297–301

    Article  Google 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):8761–8779

    Article  Google 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 13–18

  26. Lippert SK, Govindarajulu C (2015) Technological, organizational, and environmental antecedents to web services adoption. Commun IIMA 6(1):14

    Google 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 201–212

  28. Mizuno O, Hamasaki T, Takagi Y, Kikuno T (2004) An empirical evaluation of predicting runaway software projects using Bayesian classification. Springer, Berlin

    Book  Google 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 467–476

    Chapter  Google 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):3039–3059

    Article  Google 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 274–279

  32. Oztekin A et al (2013) A machine learning-based usability evaluation method for e-learning systems. Decis Support Syst 56:63–73

    Article  Google 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):775–785

    Article  Google Scholar 

  34. Sainath TN et al (2015) Deep convolutional neural networks for large-scale speech tasks. Neural Netw 64:39–48

    Article  Google Scholar 

  35. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  36. Smite D (2007) Project outcome predictions: risk barometer based on historical data. In: International conference on global software engineering (ICGSE 2007), pp 103–112

  37. Verner JM, Evanco WM, Cerpa N (2007) State of the practice: how important is effort estimation to software development success? Inf Softw Technol 49:181–193

    Article  Google Scholar 

  38. Wang Y (2007) Prediction of success in open source software development. Master, University of California

  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 122–132

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Osama Alshareet.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alshareet, O., Itradat, A., Doush, I.A. et al. Incorporation of ISO 25010 with machine learning to develop a novel quality in use prediction system (QiUPS). Int J Syst Assur Eng Manag 9, 344–353 (2018).

Download citation


  • 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)