Determining the relative importance of features for influencing software product similarity matching

Mike Mannion, Hermann Kaindl

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

    34 Downloads (Pure)

    Abstract

    As a software product line evolves a significant management challenge is comparing existing products to each other or planned products. The approach to product comparison will vary according to its purposes. One solution includes the representation of a configured product as a weighted binary string where 1 represents a feature’s presence, 0 represents its absence, and the weight represents the different levels of relative importance to the product that a feature is perceived to have. Relative importance values influence similarity matching so that the features considered important are the ones that primarily influence what is judged to be similar. A binary string similarity metric supports product comparison (a product similarity metric). For a product line that contains thousands of features the allocation of relative importance values is only practical when done automatically. This paper proposes a novel algorithm for automatically determining the relative importance of each feature. A feature tree can represent a product line in which a feature is a node in the tree and a relationship between features is an edge. A feature’s relative importance is calculated as a function of local and global tree structural measures. The local measures are the number of input and output nodes to which a feature is connected and the variability property of each of these nodes. The global measure is the distance of the feature from the root node. A mobile phone worked example illustrates the feasibility of the algorithm.
    Original languageEnglish
    Title of host publication2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)
    EditorsHossain Shahriar, Yuuichi Teranishi, Alfredo Cuzzocrea, Moushumi Sharmin, Dave Towey, AKM Jahangir Alam Majumder, Hiroki Kashiwazaki, Ji-Jiang Yang, Michiharu Takemoto, Nazmus Sakib, Ryohei Banno, Sheikh Iqbal Ahamed
    PublisherIEEE
    Pages1638-1645
    Number of pages8
    ISBN (Electronic)9798350326970
    ISBN (Print)9798350326987
    DOIs
    Publication statusPublished - 2 Aug 2023
    EventCOMPSAC 2023: The 17th IEEE International Workshop on Quality Oriented Reuse of Software - University of Turin, Turin, Italy
    Duration: 27 Jun 202330 Jun 2023
    https://ieeecompsac.computer.org/2023/about/ (Link to conference website)

    Publication series

    NameProceedings - International Computer Software and Applications Conference
    Volume2023-June
    ISSN (Print)0730-3157

    Conference

    ConferenceCOMPSAC 2023: The 17th IEEE International Workshop on Quality Oriented Reuse of Software
    Abbreviated titleQUORS 2023
    Country/TerritoryItaly
    CityTurin
    Period27/06/2330/06/23
    Internet address

    Keywords

    • binary strings
    • Feature reuse
    • product similarity

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications

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