Optimal Product Selection from Feature Models
A feature model specifies the sets of features that define valid products in a software product line. This talk explores the many-objective optimisation problem of choosing optimal products from a feature model based on user preferences. This problem has been found to be difficult for a purely search-based approach, leading to classical many-objective optimisation algorithms being enhanced by either adding in a valid product as a seed or by introducing additional mutation and replacement operators that use a SAT solver. This talk will describe the recently developed SIP method that instead enhances the search in two ways: by providing a novel representation and also by optimising first on the number of constraints that hold and only then on the other objectives.
Brief Bio:
Rob Hierons received a BA in Mathematics (Trinity College, Cambridge), and a Ph.D. in Computer Science (Brunel University). He then joined the Department of Mathematical and Computing Sciences at Goldsmiths College, University of London, before returning to Brunel University in 2000. He was promoted to full Professor in 2003.
Rob Hierons’ main research largely concerns software testing, with a focus on automated systematic testing. He also has a significant interest in program analysis and automated transformation techniques such as program slicing. He is joint Editor-in-Chief of the Journal of Software Testing, Verification, and Reliability (STVR). He has organised or been on the steering committee of several international conferences and workshops. He has published over 150 papers in international workshops, conferences and journals including in top journals such as SIAM Journal on Computing, IEEE Transactions on Computers, IEEE Transactions on Software Engineering, and ACM Transactions on Software Engineering and Methodology. He has a longstanding interest in search based software engineering.
Presentation: SIP updated