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Predictive Techniques in Software Engineering

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Encyclopedia of Machine Learning and Data Mining

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Synonyms

Predictive software models

Introduction

Software engineering (SE) is a knowledge- and decision-intensive activity. From the initial stages of the software life cycle (i.e., requirement analysis), to the later stage of testing the system, and finally maintaining the software through its operational life, decisions need to be made which impact both its success and failure. For instance, during project planning one needs to be able to forecast or predict the required resources to build the system. At the later stages such as testing or maintenance it is desirable to know which parts of the system may be impacted by a change, or are more risky or will require more intensive testing.

The process of developing software can potentially create a large amount of data and domain knowledge. The nature of the data, of course, depends on the phase in which the data were generated. During the requirement analysis, this data most times is manifested in the form of documentations. As the...

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Recommended Reading

  • Briand L, El Emam K, Surmann D, Wieczorek I (1999) An assessment and comparison of common software cost estimation modeling techniques. In: Proceedings of 21st international conference on software engineering, Los Angeles, pp 313–322

    Google Scholar 

  • Cohen W, Devanbu P (1999) Automatically exploring hypotheses about fault prediction: a comparative study of inductive logic programming methods. Int J Softw Eng Knowl Eng 9(5):519–546

    Article  Google Scholar 

  • Dolado JJ (2000) A validation of the component-based method for software size estimation. IEEE Trans Softw Eng 26(10):1006–1021

    Article  Google Scholar 

  • El Emam K, Benlarbi S, Goel N, Rai S (2001) Comparing case-based reasoning classifiers for predicting high risk software components. J Syst Softw 55(3): 301–320

    Article  Google Scholar 

  • Esteva JC (1990) Learning to recognize reusable software modules using an inductive classification system. In: Proceedings of the fifth Jerusalem conference on information technology, Jerusalem, pp 278–285

    Google Scholar 

  • Evett M, Khoshgoftar T (1998) GP-based software quality prediction. In: Proceedings of the third annual conference on genetic programming, pp 60–65

    Google Scholar 

  • Fenton N, Neil M (1999) A critique of software defect prediction models. IEEE Trans Softw Eng 25(5):675–689

    Article  Google Scholar 

  • Fenton NE, Pfleeger SL (1998) Software metrics: a rigorous and practical approach, 2nd edn. PWS, Boston

    Google Scholar 

  • Ganek AG, Corbi TA (2003) The dawning of autonomic computing era. IBM Syst J 42(1):5–18

    Article  Google Scholar 

  • Grosser D, Sahraoui HA, Valtchev P (2002) Predicting software stability using case-based reasoning. In: Proceedings of 17th IEEE international conference on automated software engineering (ASE), Edinburgh, pp 295–298

    Google Scholar 

  • Khoshgoftaar T, Allen E, Hudepohl J, Aud S (1997) Applications of neural networks to software quality modeling of a very large telecommunications system. IEEE Trans Neural Netw 8(4):902–909

    Article  Google Scholar 

  • Ling C, Sheng V, Bruckhaus T, Madhavji N (2006) Maximum profit mining and its application in software development. In: Proceedings of the 12th ACM international conference on knowledge discovery and data mining (SIGKDD), Philadelphia, pp 929–934

    Google Scholar 

  • Mao Y, Sahraoui H, Lounis H (1998) Reusability hypothesis verification using machine learning techniques: a case study. In: Proceedings of the 13th IEEE international conference on automated software engineering, Honolulu, pp 84–93

    Google Scholar 

  • Oliveira A (2006) Estimation of software project effort with support vector regression. Neurocomputing 69(13–15):1749–1753

    Article  Google Scholar 

  • Padberg F, Ragg T, Schoknecht R (2004) Using machine learning for estimating the defect content after an inspection. IEEE Trans Softw Eng 30(1):17–28

    Article  Google Scholar 

  • Pai PF, Hong WC (2006) Software reliability forecasting by support vector machines with simulated annealing algorithms. J Syst Softw 79(6):747–755

    Article  Google Scholar 

  • Pendharkar PC (2004) An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models. Inf Manag 42(1):61–73

    Article  Google Scholar 

  • Pfleeger SL, Atlee JM (2003) Software engineering: theory and practice. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Quah TS, Thwin MMT (2003) Application of neural networks for software quality prediction using object-oriented metrics. In: Proceedings of international conference on software maintenance, Amsterdam, pp 22–26

    Google Scholar 

  • Regolin EN, de Souza GA, Pozo ART, Vergilio SR (2003) Exploring machine learning techniques for software size estimation. In: Proceedings of the 23rd international conference of the Chilean computer science society (SCCC), Chillan, pp 130–136

    Google Scholar 

  • Sayyad Shirabad J, Lethbridge TC, Matwin S (2007) Modeling relevance relations using machine learning techniques. In: Tsai J, Zhang D (eds) Advances in machine learning applications in software engineering. IGI, pp 168–207

    Google Scholar 

  • Seliya N, Khoshgoftaar TM (2007) Software quality estimation with limited fault data: a semi-supervised learning perspective. Softw Qual J 15(3):327–344

    Article  Google Scholar 

  • Shepperd M, Schofield C (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23(11):736–743

    Article  Google Scholar 

  • Sitte R (1999) Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration. IEEE Trans Reliab 48(3):285–291

    Article  Google Scholar 

  • Xing F, Guo P, Lyu MR (2005) A novel method for early software quality prediction based on support vector machine. In: Proceedings of IEEE international conference on software reliability engineering, Chicago, pp 213–222

    Google Scholar 

  • Zhang Du, Tsai JP (2003) Machine learning and software engineering. Softw Qual J 11(2):87–119

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Ottawa, Ottawa, ON, Canada

    Jelber Sayyad Shirabad

Authors
  1. Jelber Sayyad Shirabad
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Shirabad, J.S. (2017). Predictive Techniques in Software Engineering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_661

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