AuthorsS. A. Safdar, H. Lu, T. Yue and S. Ali
TitleMining Cross Product Line Rules with Multi-Objective Search and Machine Learning
AfilliationSoftware Engineering
Project(s)Zen-Configurator: Interactive and Optimal Configuration of Cyber Physical System Product Lines , The Certus Centre (SFI)
Publication TypeProceedings, refereed
Year of Publication2017
Conference Name Genetic and Evolutionary Computation Conference (GECCO)
KeywordsRule Mining; Multi-Objective Search; Configuration; Machine Learning; Product Line.

Nowadays, an increasing number of systems are being developed by integrating products (belonging to different products lines) that communicate with each other through information networks. Cost-effectively supporting Product Line Engineering (PLE) and in particular enabling automation of configuration in PLE is a challenge. Capturing rules is the key for enabling automation of configuration. Product configuration has a direct impact on runtime interactions of communicating products. Such products might be within or across product lines and there usually don’t exist explicitly specified rules constraining configurable parameter values of such products. Manually specifying such rules is tedious, time-consuming, and requires expert’s knowledge of the domain and the product lines. To address this challenge, we propose an approach named as SBRM that combines multi- objective search with machine learning to mine rules. To evaluate the proposed approach, we performed a real case study of two communicating Video Conferencing Systems belonging to two different product lines. Results show that SBRM performed significantly better than Random Search in terms of fitness values, Hyper-Volume, and machine learning quality measurements. When comparing with rules mined with real data, SBRM performed significantly better particularly in terms of Failed Precision (18%), Failed Recall (72%), and Failed F-measure (59%).

Citation Key25221

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