Model-Driven Optimization with a Focus on the Effectiveness and Efficiency of Evolutionary Algorithms

Optimization problems are ubiquitous in software engineering. They arise, for example, when searching for a modular software design or planning a cost-efficient development process. Search-based software engineering (SBSE) is concerned with solving optimization problems by applying search-based algorithms. Among the most popular are evolutionary algorithms, which are the focus of this thesis. Following the example of natural evolution, they use selection, mutation, and crossover operators to evolve existing solutions. In the hope of enabling the use of SBSE without optimization expertise, model-driven optimization (MDO) relies on model-driven engineering (MDE); models and model transformations are used to specify optimization problems and solution algorithms. Two ways of representing solutions, the model-based approach (MB-MDO) and the rule-based approach, have established. However, the implications of choosing one over the other are not clear. Therefore, we compare both approaches qualitatively and quantitatively and pursue MB-MDO as the more promising approach in the rest of the thesis. How to design efficient and effective evolutionary algorithms is a central question in MB-MDO. Moreover, how to perform crossover there is not yet known. We first present a framework that highlights and explains the core concepts of evolutionary algorithms in MB-MDO and formalizes them based on graph transformation theory. It not only contributes to the understanding of evolutionary algorithms in MB-MDO, but in particular facilitates their precise specification, analysis, and evaluation. The framework is used to define important properties of mutation operators and to evaluate their impact on the efficiency and effectiveness of evolutionary algorithms. Furthermore, a general, graph-based approach for the construction of crossover operators in MB-MDO is presented. The general approach is also concretized for the Eclipse Modeling Framework (EMF). Finally, an evaluation of a prototypical implementation shows the relevance of crossover operators for evolutionary algorithms in MB-MDO

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Some issues in the 'archaeology' of software evolution

During a software project's lifetime, the software goes through many changes, as components are added, removed and modified to fix bugs and add new features. This paper is intended as a lightweight introduction to some of the issues arising from an `archaeological' investigation of software evolution. We use our own work to look at some of the challenges faced, techniques used, findings obtained, and lessons learnt when measuring and visualising the historical changes that happen during the evolution of software