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

Non-remote reference noise cancellation - using reference data in the presence of surface-NMR signals

Surface-NMR measurements commonly suffer from low signal-to-noise ratios. In recent years, with the introduction of multi-channel surface-NMR instruments, the technique of remote-reference noise cancellation (RNC) was developed and significantly improved the applicability of surface-NMR. The current formulation of RNC requires a reference loop to be placed a remote distance from the transmitter loop such that no NMR signal is recorded. Reference loops placed at non-remote distances have been envisaged to provide both improved noise cancellation performance and field efficiency; however, the concept has not been previously applied because the theoretical framework was missing. In this paper, the theoretical framework is presented. It is demonstrated that reference loops placed at non-remote distances provide superior noise cancellation performance. Considerations for placing the reference loop relative to the transmitter loop are provided, and the theoretical framework is evaluated based on a semi-synthetic example using real field noise and synthetic surface-NMR data. © 202

Improving responsiveness of Android activity navigation via genetic improvement

Responsiveness issues are one of the key reasons why mobile phone users abandon an app or leave bad reviews. In this work, we explore the use of Genetic Improvement to automatically refactor applications to reduce the time taken to move between and within Android activities, without affecting their functionality. This particular Android responsiveness issue has not previously been tackled before. With its application directly to source code, our approach can be used to complement previous work, which modifies the operating system, or focuses on detection of specific coding patterns. We present a fully automated technique for finding improvements to this responsiveness, which does not require the use of an Android device or emulator. We apply our approach to 7 real-world open source applications and find improvements of up to 30% in navigation response time

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