Justyna Petke

Genetic Improvement – new direction in SBSE

Software engineering problems can often be reformulated as search problems. One seeks to find an optimal or near optimal solution in a wide range of candidate solutions, guided by a fitness function that distinguishes between good and bad solutions.

The talk will cover a new exciting direction in search-based software engineering, namely genetic improvement. GI uses automated search in order to improve existing software. It has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Work on genetic improvement has led to several awards, ranging from best paper awards to several `Humie’ awards given for human-competitive results produced by genetic and evolutionary computation.

I will give an overview of genetic improvement and present key components of a GI framework. This keynote is based on work conducted at the CREST centre at UCL.

Brief Bio:

Justyna Petke is a Senior Research Associate at the Centre for Research on Evolution, Search and Testing (CREST) at University College London (UCL). She is interested in the connections between constraint satisfaction and search-based software engineering. Her current research focuses on genetic improvement and combinatorial interaction testing. She won several awards for her work on GI: Silver and Gold `Humie` at GECCO 2014 and 2016 and an ACM SIGSOFT Distinguished Paper Award at ISSTA 2015.

 

Presentation: sbsekeynote_v2.compressed

Robert Hierons

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