Genetic Improvement (GI) is an area of Search Based Software Engineering which seeks to improve software's nonfunctional properties by treating program code as if it were genetic material which is then evolved to produce more optimal solutions. Hitherto, the majority of focus has been on optimising program's execution time which, though important, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastructure, and ecological concerns are forcing developers to focus on the energy their software consumes. We report on investigations into using GI to automatically find more energy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25%.

Modern software is bloated. Demand for new functionality has led developers to include more and more features, many of which become unneeded or unused as software evolves. This phenomenon, known as software bloat, results in software consuming more resources than it otherwise needs to. How to effectively and automatically debloat software is a long-standing problem in software engineering. Various debloating techniques have been proposed since the late 1990s. However, many of these techniques are built upon pure static analysis and have yet to be extended and evaluated in the context of modern Java applications where dynamic language features are prevalent. To this end, we develop an end-to-end bytecode debloating framework called JShrink. It augments traditional static reachability analysis with dynamic profiling and type dependency analysis and renovates existing bytecode transformations to account for new language features in modern Java. We highlight several nuanced technical challenges that must be handled properly and examine behavior preservation of debloated software via regression testing. We find that (1) JShrink is able to debloat our real-world Java benchmark suite by up to 47% (14% on average); (2) accounting for dynamic language features is indeed crucial to ensure behavior preservationÐreducing 98% of test failures incurred by a purely static equivalent, Jax, and 84% for ProGuard; and (3) compared with purely dynamic approaches, integrating static analysis with dynamic profiling makes the debloated software more robust to unseen test executionsÐin 22 out of 26 projects, the debloated software ran successfully under new tests.

Abstract-Reducing the energy consumption of software systems though optimisations techniques such as genetic improvement is gaining interest. However, efficient and effective improvement of software systems requires a better understanding of the code-change search space. One important choice practitioners have is whether to preserve the system's original output or permit approximation with each scenario having its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by the modification operators is 2.69% (0.76% on average). By contrast, this figure increases dramatically to 95.60% (33.90% on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for code optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied source code modifications produce an effect greater than their individual sum. Our results reveal that 12.0% of all joint code modifications produced such a synergistic effect though 38.5% produce an antagonistic interaction in which simultaneously applied modifications are less effective than when applied individually. This highlights the need for more advanced search-based approaches.

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Abstract. OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a tradeoff between execution time and classification accuracy. Our results show that execution time can be decreased by 48% if a 1.80% classification inaccuracy is permitted (compared to 1.04% classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user.

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Abstract. We give a model of parallel distributed genetic improvement. With modern low cost power monitors; high speed Ethernet LAN latency and network jitter have little effect. The model calculates a minimum usable mutation effect based on the analogue to digital converter (ADC)'s resolution and shows the optimal test duration is inversely proportional to smallest impact we wish to detect. Using the example of a 1KHz 12 bit 0.4095 Amp ADC optimising software energy consumption we find: it will be difficult to detect mutations which an average effect less than 58 microA, and typically experiments should last well under a second.

As software projects grow in complexity, they come packaged with under-utilized libraries and therefore become bloated. Though several software debloating tools exist, none of them help developers gain insights into how under-utilized those libraries are nor help developers build conidence in the behavior preservation of software after debloating. To bridge this gap, we developed WebJShrink, a visual analytics tool for analyzing and pruning bloated software projects. WebJShrink is built on JShrink which uses static and dynamic reachability analysis to determine the extent of software bloat. WebJShrink provides rich visualizations of the bloat lurking within a target project's internal structure. It then removes unused features, and returns a safer, slimmer variant of the software project. To illustrate the target project's behavior preservation, We-bJShrink examines the debloated software with its JUnit tests and visualizes the test results. In evaluating WebJShrink against 26 real world systems, we found WebJShrink could reduce software size by up to 42%, 11% on average, while still passing 100% of unit tests after debloating. We provide a video demonstrating WebJShrink at https://youtu.be/yzVzcd-MJ1w. CCS CONCEPTS • Software and its engineering → Automatic programming; Object oriented languages.