Feature weighting for Case-based reasoning software project effort estimation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    Background: Software effort estimation is one of the most important activities in software development process. Un-fortunately, estimates are often substantially wrong and specifically most projects encounter effort overruns. Numerous methods have been proposed including Case based reasoning (CBR). Existing research shows that feature subset selection (FSS) is an important aspect of CBR, however, searching for the optimal feature weights is a combinatorial problem and therefore NP-hard. Objective: To develop and evaluate efficient algorithms to generalise FSS into an effective feature weighting approach that can improve accuracy further, since not all features contribute equally to solving the problem. Method: Use various search algorithms e.g., forward sequential weighting (FSW) and random mutation hill climbing (RMHC) to assign weight to features in order to improve the estimation accuracy. We will extend an existing CBR java shell ArchANGEL1. We will perform experiments based on repeated measures design on real world datasets to evaluate these algorithms. Limitations of the proposed research: Dataset quality cannot be assured therefore our findings could be influenced by noisy data. Older datasets may be misrepresenting current software development approaches and technologies. CBR could be sensitive to the choice of distance metric; however, we will only use standardised Euclidean distance.

    Original languageEnglish
    Title of host publication18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014
    PublisherAssociation for Computing Machinery
    ISBN (Print)9781450324762
    DOIs
    Publication statusPublished - 2014
    Event18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014 - London, United Kingdom
    Duration: May 12 2014May 14 2014

    Other

    Other18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014
    CountryUnited Kingdom
    CityLondon
    Period5/12/145/14/14

    Fingerprint

    Case based reasoning
    Software engineering
    Experiments

    All Science Journal Classification (ASJC) codes

    • Human-Computer Interaction
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Software

    Cite this

    Sigweni, B. (2014). Feature weighting for Case-based reasoning software project effort estimation. In 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014 [a54] Association for Computing Machinery. https://doi.org/10.1145/2601248.2613081
    Sigweni, Boyce. / Feature weighting for Case-based reasoning software project effort estimation. 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014. Association for Computing Machinery, 2014.
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    abstract = "Background: Software effort estimation is one of the most important activities in software development process. Un-fortunately, estimates are often substantially wrong and specifically most projects encounter effort overruns. Numerous methods have been proposed including Case based reasoning (CBR). Existing research shows that feature subset selection (FSS) is an important aspect of CBR, however, searching for the optimal feature weights is a combinatorial problem and therefore NP-hard. Objective: To develop and evaluate efficient algorithms to generalise FSS into an effective feature weighting approach that can improve accuracy further, since not all features contribute equally to solving the problem. Method: Use various search algorithms e.g., forward sequential weighting (FSW) and random mutation hill climbing (RMHC) to assign weight to features in order to improve the estimation accuracy. We will extend an existing CBR java shell ArchANGEL1. We will perform experiments based on repeated measures design on real world datasets to evaluate these algorithms. Limitations of the proposed research: Dataset quality cannot be assured therefore our findings could be influenced by noisy data. Older datasets may be misrepresenting current software development approaches and technologies. CBR could be sensitive to the choice of distance metric; however, we will only use standardised Euclidean distance.",
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    Sigweni, B 2014, Feature weighting for Case-based reasoning software project effort estimation. in 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014., a54, Association for Computing Machinery, 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014, London, United Kingdom, 5/12/14. https://doi.org/10.1145/2601248.2613081

    Feature weighting for Case-based reasoning software project effort estimation. / Sigweni, Boyce.

    18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014. Association for Computing Machinery, 2014. a54.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AB - Background: Software effort estimation is one of the most important activities in software development process. Un-fortunately, estimates are often substantially wrong and specifically most projects encounter effort overruns. Numerous methods have been proposed including Case based reasoning (CBR). Existing research shows that feature subset selection (FSS) is an important aspect of CBR, however, searching for the optimal feature weights is a combinatorial problem and therefore NP-hard. Objective: To develop and evaluate efficient algorithms to generalise FSS into an effective feature weighting approach that can improve accuracy further, since not all features contribute equally to solving the problem. Method: Use various search algorithms e.g., forward sequential weighting (FSW) and random mutation hill climbing (RMHC) to assign weight to features in order to improve the estimation accuracy. We will extend an existing CBR java shell ArchANGEL1. We will perform experiments based on repeated measures design on real world datasets to evaluate these algorithms. Limitations of the proposed research: Dataset quality cannot be assured therefore our findings could be influenced by noisy data. Older datasets may be misrepresenting current software development approaches and technologies. CBR could be sensitive to the choice of distance metric; however, we will only use standardised Euclidean distance.

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    Sigweni B. Feature weighting for Case-based reasoning software project effort estimation. In 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014. Association for Computing Machinery. 2014. a54 https://doi.org/10.1145/2601248.2613081