Portfolio Evolution Analysis for SPL Scoping: Unveiling the dynamics with dedicated time-series dashboards
Journal of Systems and Software, ISSN: 0164-1212, Vol: 220, Page: 112259
2025
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Article Description
Software Product Line Engineering (SPLE) is a recognized methodology for systematically developing reusable software components and tailored software products for specific market segments. A critical activity within domain engineering is SPL Scoping, aimed at profiling the SPL boundaries and fitting this market segment. Yet, this market is a moving target, which requires SPLs to be proactive in anticipating and addressing future challenges and opportunities. In this setting, rather than all the possible products potentially derived from the SPL’s Feature Model (i.e., the potential portfolio), the subset of products actually deployed is a better indicator of market adoption and customer preferences, and ultimately, of the SPL’s fitness to the market. This study characterizes the phenomenon of ‘deployed portfolio evolution’ in terms of the size and frequency of portfolio changes. We then elaborate on the advantages of utilizing this data source and the necessity for dedicated tools for Portfolio Evolution Analysis integrated within variability managers. To achieve this, we introduce the functional requirements, implementation, and architecture of PortfolioScan, a dashboard for Portfolio Evolution Analysis built on top of pure::variants. The dashboard enables the evaluation of feature usage through time-series charts such as temporal heat maps, line charts, and also a scoping-criteria comparison matrix. Evaluation results (n=13) indicate a positive perception. Practitioners also recognize the benefits of Portfolio Evolution Analysis for Testing Resource Optimization and Configuration Assistance. The ultimate goal of the study is to establish a basis for more responsive and market-oriented decision-making in SPL tasks.
Bibliographic Details
Elsevier BV
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