Comparing Genetic Programming Approaches for Non-functional Genetic Improvement

Genetic improvement (GI) uses automated search to find improved versions of existing software. While most GI work use genetic programming (GP) as the underlying search process, focus is usually given to the target software only. As a result, specifics of GP algorithms for GI are not well understood and rarely compared to one another. In this work, we propose a robust experimental protocol to compare different GI search processes and investigate several variants of GP- and random-based approaches. Through repeated experiments, we report a comparative analysis of these approaches, using one of the previously used GI scenarios: improvement of runtime of the MiniSAT satisfiability solver. We conclude that the test suites used have the most significant impact on the GI results. Both random and GP-based approaches are able to find improved software, even though the percentage of viable software variants is significantly smaller in the random case ( 14.5% vs. 80.1%). We also report that GI produces MiniSAT variants up to twice as fast as the original on sets of previously unseen instances from the same application domain

New operators for non-functional genetic improvement

Genetic improvement uses automated search to find improved versions of existing software. Typically software is modified using either delete, copy or replace operations at the level of source code, its abstract syntax tree, binary or assembly representaion. Impressive improvements have been achieved through this approach, yet research in the use of other search operators is largely unexplored. We propose several ways for devising new search operators for improvement of non-functional properties using a genetic improvement apporach

Genetic Improvement for Code Obfuscation

Genetic improvement (GI) is a relatively new area of software engineering and thus the extent of its applicability is yet to be explored. Although a growing interest in GI in recent years started with the work on automatic bug fixing, the area flourished when results on optimisation of non-functional software properties, such as efficiency and energy consumption, were published. Further success of GI in transplanting functionality from one program to another leads to a question: what other software engineering areas can benefit from the use of genetic improvement techniques? We propose to utilise GI for code obfuscation

Improving Android App Responsiveness through Search-Based Frame Rate Reduction

Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in the application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43%), often applications’ test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps’ responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements

Optimising SQL Queries Using Genetic Improvement

Structured Query Language (SQL) queries are ubiquitous in modern software engineering. These queries can be costly when run on large databases with many entries and tables to consider. We propose using Genetic Improvement (GI) to explore patches for these queries, with the aim of optimising their execution time, whilst maintaining the functionality of the program in which they are utilised. Specifically, we propose three ways in which SQL JOIN statements can be mutated in order to improve performance. We also discuss the requirements of software being improved in this manner and the potential challenges of our approach

Message from the demonstrations track chairs of ICSE 2019

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record

Empirical Comparison of Search Heuristics for Genetic Improvement of Software

Genetic improvement uses automated search to improve existing software. It has been successfully used to optimise various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. Genetic improvement typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this paper, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of 8 improvement scenarios. Additionally, we also provide new genetic improvement benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than genetic programming approaches. Moreover, improvements were found in all scenarios (between 15% and 68%). A replication package can be found online: https://github.com/bloa/tevc _2020 artefact

Evolving better RNAfold structure prediction

Grow and graft genetic programming (GGGP) evolves more than 50000 parameters in a state-of-the-art C program to make functional source code changes which give more accurate predictions of how RNA molecules fold up. Genetic improvement updates 29% of the dynamic programming free energy model parameters. In most cases (50.3%) GI gives better results on 4655 known secondary structures from RNA_STRAND (29.0% are worse and 20.7% are unchanged). Indeed it also does better than parameters recommended by Andronescu, M., et al.: Bioinformatics 23(13) (2007) i19–i28