Early Software Quality Prediction Based on Software Requirements Specification Using Fuzzy Inference System

  • Muhammad Hammad Masood
  • Malik Jahan KhanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


Software Requirements Specification (SRS) is the key fundamental document formally listing down the customer expectations from the software to be built. Any weakness or fault injected at this stage in the requirements is expected to ripple towards the following phases of software development life cycle resulting in development of a software system of poor quality. Software quality prediction promises to raise alarms about the quality of the end product at earlier stages. It becomes more challenging as we move earlier in stages because of limited information is available at earlier stages. Therefore little effort has been put in literature to predict software quality at SRS stage. This position paper presents a novel approach of prediction of software quality using SRS. SRS document is converted into a graph and different parameters including readability index, complexity, size and an estimation of coupling are extracted. These parameters are fed into a Fuzzy Inferencing System (FIS) to predict the quality of the end product. The proposed model has been evaluated on a sample of student projects and has shown reasonable performance.


Software quality prediction Software requirements Fuzzy logic 


  1. Albrecht, A.J., Gaffney, J.E.: Software functions, source lines of codes and development effort prediction: a software science validation. IEEE Trans. Softw. Eng. 9(11), 639–648 (1983)CrossRefGoogle Scholar
  2. Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: Third International AAAI Conference on Weblogs and Social Media (2009)Google Scholar
  3. Christopher, D.F.X., Chandra, E.: Prediction of software requirements stability based on complexity point measurement using multi-criteria fuzzy approach. Int. J. Softw. Eng. Appl. 3(6), 101–115 (2012)Google Scholar
  4. Dargan, J.L., Campos-Nanez, E., Fomin, P., Wasek, J.: Predicting systems performance through requirements quality attributes model. Procedia Comput. Sci. 28, 347–353 (2014)CrossRefGoogle Scholar
  5. Divinagracia, H.R.: FP calculator (2000).
  6. Gephi: The open graph viz platform (2015). Accessed 27 Jan 2016
  7. Grineva, M., Grinev, M., Lizorkin, D.: Extracting key terms from noisy and multitheme documents. In: Proceedings of the 18th International Conference on World Wide Web, pp. 661–670 (2009)Google Scholar
  8. Hovorushchenko, T., Krasiy, A.: Method of evaluating the success of software project implementation based on analysis of specification using neuronet information technologies (2015)Google Scholar
  9. Kitchenham, B., Pfleeger, S.L.: Software quality: the elusive target. IEEE Softw. 13(1), 12–21 (1996)CrossRefGoogle Scholar
  10. Klaus, P.: Requirements Engineering Fundamentals, Principles, and Techniques. Springer, Heidelberg (2010)Google Scholar
  11. Semantic-Knowledge: High performance text analysis for professional users (2014).
  12. Lami, G., Gnesi, S., Fabbrini, F.: An automatic tool for the analysis of natural language requirements. Informe tecnico, CNR (2004)Google Scholar
  13. Misra, S.: Weyuker’s properties, language independency and object oriented metrics. In: Gervasi, O., et al. (eds.) ICCSA 2009. LNCS, vol. 5593, pp. 70–81. Springer, Heidelberg (2009). Scholar
  14. Pandey, A.K., Goyal, N.K.: Fault prediction model by fuzzy profile development of reliability relevant software metrics. Int. J. Comput. Appl. 11(6), 34–41 (2010)Google Scholar
  15. Pandey, A.K., Goyal, N.K.: Early Software Reliability Prediction. Springer, India (2013). Scholar
  16. QuARS: Quality analyzer for requirement specifications (2009).
  17. Measure text readability (2016).
  18. Sana, S., Hassan, A., Malik J.K., Shafay S.: Software quality prediction techniques: a comparative analysis. In: International Conference on Emerging Technologies, pp. 18–19 (2008)Google Scholar
  19. Sharma, A., Kushwaha, D.: Complexity measure based on requirement engineering document and its validation. In: International Conference on Computer and Communication Technology, pp. 608–615 (2010)Google Scholar
  20. Sharma, A., Kushwaha, D.S.: Applying requirement based complexity for the estimation of software development and testing effort. SIGSOFT Softw. Eng. Notes 37(1), 1–11 (2012)CrossRefGoogle Scholar
  21. Smidts, C., Stoddard, R.W., Stutzke, M.: Software reliability models: an approach to early reliability prediction. In: Proceedings of the Seventh International Symposium on Software Reliability Engineering, pp. 132–141 (1996)Google Scholar
  22. Software Engineering Standards Committee of the IEEE Computer Society: IEEE Recommended Practice for Software Requirements Specifications (1998)Google Scholar
  23. Stanford: Stanford parser (2015). Accessed 26 Jan 2016
  24. Suanmali, L., Salim, N., Binwahlan, M.S.: Fuzzy logic based method for improving text summarization. J. Comput. Sci. 2(1), 6 (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceNamal CollegeMianwaliPakistan

Personalised recommendations