A Survey of Genetic Improvement Search Spaces

Genetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve software's running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GI's mutation search space. This paper summarises the work published on this question to date

Injecting Shortcuts for Faster Running Java Code

Genetic Improvement of software applies search methods to existing software to improve the target program in some way. Impressive results have been achieved, including substantial speedups, using simple operations that replace, swap and delete lines or statements within the code. Often this is achieved by specialising code, removing parts that are unnecessary for particular use-cases. Previous work has shown that there is a great deal of potential in targeting more specialised operations that modify the code to achieve the same functionality in a different way. We propose six new edit types for Genetic Improvement of Java software, based on the insertion of break, continue and return statements. The idea is to add shortcuts that allow parts of the program to be skipped in order to speed it up. 10000 randomly-generated instances of each edit were applied to three open-source applications taken from GitHub. The key findings are: (1) compilation rates for inserted statements without surrounding "if" statements are 1.5-18.3%; (2) edits where the insert statement is embedded within an "if" have compilation rates of 3.2-55.8%; (3) of those that compiled, all 6 edits have a high rate of passing tests (Neutral Variant Rate), >60% in all but one case, and so have the potential to be performance improving edits. Finally, a preliminary experiment based on local search shows how these edits might be used in practice

Dissipative polynomials

Limited precision floating point computer implementations of large polynomial arithmetic expressions are nonlinear and dissipative. They are not reversible (irreversible, lack conservation), lose information, and so are robust to perturbations (anti-fragile) and resilient to fluctuations. This gives a largely stable locally flat evolutionary neutral fitness search landscape. Thus even with a large number of test cases, both large and small changes deep within software typically have no effect and are invisible externally. Shallow mutations are easier to detect but their RMS error need not be simple

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

Genetic improvement: Taking real-world source code and improving it using genetic programming

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