API-constrained genetic improvement

ACGI respects the Application Programming Interface whilst using genetic programming to optimise the implementation of the API. It reduces the scope for improvement but it may smooth the path to GI acceptance because the programmer’s code remains unaffected; only library code is modified.We applied ACGI to C++ software for the stateof-the-art OpenCV SEEDS superPixels image segmentation algorithm, obtaining a speed-up of up to 13.2% (±1.3%) to the $50K Challenge winner announced at CVPR 2015

Reinforcement Learning for the Unit Commitment Problem

In this work we solve the day-ahead unit commitment (UC) problem, by formulating it as a Markov decision process (MDP) and finding a low-cost policy for generation scheduling. We present two reinforcement learning algorithms, and devise a third one. We compare our results to previous work that uses simulated annealing (SA), and show a 27% improvement in operation costs, with running time of 2.5 minutes (compared to 2.5 hours of existing state-of-the-art).Comment: Accepted and presented in IEEE PES PowerTech, Eindhoven 2015, paper ID 46273

Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of domain specific concepts, by providing developers with specific tools for their domain of expertise. A natural approach to create DSLs is to reuse existing modeling standards and tools. In this area, the Eclipse Modeling Framework (EMF) has rapidly become the defacto standard in the MDSE for building Domain Specific Languages (DSL) and tools based on generative techniques. However, the use of EMF generated tools in domains like Internet of Things (IoT), Cloud Computing or Models@Runtime reaches several limitations. In this paper, we identify several properties the generated tools must comply with to be usable in other domains than desktop-based software systems. We then challenge EMF on these properties and describe our approach to overcome the limitations. Our approach, implemented in the Kevoree Modeling Framework (KMF), is finally evaluated according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014

Parametric, Optimization-Based Study on the Feasibility of a Multisegment Antisolvent Crystallizer for in Situ Fines Removal and Matching of Target Size Distribution

Peer reviewedPostprin

An Empirical Study of Cohesion and Coupling: Balancing Optimisation and Disruption

Search based software engineering has been extensively applied to the problem of finding improved modular structures that maximise cohesion and minimise coupling. However, there has, hitherto, been no longitudinal study of developers’ implementations, over a series of sequential releases. Moreover, results validating whether developers respect the fitness functions are scarce, and the potentially disruptive effect of search-based remodularisation is usually overlooked. We present an empirical study of 233 sequential releases of 10 different systems; the largest empirical study reported in the literature so far, and the first longitudinal study. Our results provide evidence that developers do, indeed, respect the fitness functions used to optimise cohesion/coupling (they are statistically significantly better than arbitrary choices with p << 0.01), yet they also leave considerable room for further improvement (cohesion/coupling can be improved by 25% on average). However, we also report that optimising the structure is highly disruptive (on average more than 57% of the structure must change), while our results reveal that developers tend to avoid such disruption. Therefore, we introduce and evaluate a multi-objective evolutionary approach that minimises disruption while maximising cohesion/coupling improvement. This allows developers to balance reticence to disrupt existing modular structure, against their competing need to improve cohesion and coupling. The multi-objective approach is able to find modular structures that improve the cohesion of developers’ implementations by 22.52%, while causing an acceptably low level of disruption (within that already tolerated by developers)