A novel composite model approach to improve software quality prediction

Salah Bouktif, Faheem Ahmed, Issa Khalil, Giuliano Antoniol

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)


    Context:: How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances. Objective: The main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques. Method: In this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a "composite" model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances. Results: The results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones. Conclusion: Our approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.

    Original languageEnglish
    Pages (from-to)1298-1311
    Number of pages14
    JournalInformation and Software Technology
    Issue number12
    Publication statusPublished - Dec 2010


    • Decision trees
    • Fault-proneness
    • Genetic algorithm
    • Software quality prediction

    ASJC Scopus subject areas

    • Software
    • Information Systems
    • Computer Science Applications


    Dive into the research topics of 'A novel composite model approach to improve software quality prediction'. Together they form a unique fingerprint.

    Cite this