Enhancing Genetic Improvement of Software with Regression Test Selection

Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 78% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area

The Symposium on Search-Based Software Engineering: Past, Present and Future

CONTEXT: Search-Based Software Engineering (SBSE) is the research field where Software Engineering (SE) problems are modelled as search problems to be solved by search-based techniques. The Symposium on Search Based Software Engineering (SSBSE) is the premier event on SBSE, which had its 11th edition in 2019. OBJECTIVE: In order to better understand the characteristics and evolution of papers published at SSBSE, this work reports results from a mapping study targeting the proceedings of all SSBSE editions. Despite the existing mapping studies on SBSE, our contribution in this work is to provide information to researchers and practitioners willing to enter the SBSE field, being a source of information to strengthen the symposium, guide new studies, and motivate new collaboration among research groups. METHOD: A systematic mapping study was conducted with a set of four research questions, in which 134 studies published in all editions of SSBSE, dated from 2009 to 2019, were evaluated. In a fifth question, 32 papers published in the challenge track were summarised. RESULTS: Throughout the years, 290 authors from 25 countries have contributed to the main track of the symposium, with the collaboration of at least two institutions in 46.3% of the papers. SSBSE papers have got substantial external visibility, as most citations are from different venues. The SE tasks addressed by SSBSE are mostly related to software testing, software debugging, software design, and maintenance. Evolutionary algorithms are present in 75% of the papers, being the most common search technique. The evaluation of the SBSE approaches usually includes industrial systems. CONCLUSIONS: SSBSE has helped increase the popularity of SBSE in the SE research community and has played an important role on making SBSE mature. There are still problems and challenges to be addressed in the SBSE field, which can be tackled by SSBSE authors in further studies

Search-based Unit Test Generation for Evolving Software

Search-based software testing has been successfully applied to generate unit test cases for object-oriented software. Typically, in search-based test generation approaches, evolutionary search algorithms are guided by code coverage criteria such as branch coverage to generate tests for individual coverage objectives. Although it has been shown that this approach can be effective, there remain fundamental open questions. In particular, which criteria should test generation use in order to produce the best test suites? Which evolutionary algorithms are more effective at generating test cases with high coverage? How to scale up search-based unit test generation to software projects consisting of large numbers of components, evolving and changing frequently over time? As a result, the applicability of search-based test generation techniques in practice is still fundamentally limited. In order to answer these fundamental questions, we investigate the following improvements to search-based testing. First, we propose the simultaneous optimisation of several coverage criteria at the same time using an evolutionary algorithm, rather than optimising for individual criteria. We then perform an empirical evaluation of different evolutionary algorithms to understand the influence of each one on the test optimisation problem. We then extend a coverage-based test generation with a non-functional criterion to increase the likelihood of detecting faults as well as helping developers to identify the locations of the faults. Finally, we propose several strategies and tools to efficiently apply search-based test generation techniques in large and evolving software projects. Our results show that, overall, the optimisation of several coverage criteria is efficient, there is indeed an evolutionary algorithm that clearly works better for test generation problem than others, the extended coverage-based test generation is effective at revealing and localising faults, and our proposed strategies, specifically designed to test entire software projects in a continuous way, improve efficiency and lead to higher code coverage. Consequently, the techniques and toolset presented in this thesis - which provides support to all contributions here described - brings search-based software testing one step closer to practical usage, by equipping software engineers with the state of the art in automated test generation

Carabid beetles (Coleoptera, Carabidae) as indicators of environmental change in Ranomafana National Park, Madagascar

Growing human populations and increasing exploitation of natural resources threaten nature all over the world. Tropical countries are especially vulnerable to human impact because of the high number of species, most of these endemic and still unknown. Madagascar is one of the centers of high biodiversity and renowned for its unique species. However, during the last centuries many endemic species have gone extinct and more are endangered. Because of high natural values, Madagascar is one of the global conservation priorities. The establishment of Ranomafana National Park (RNP) was intended to preserve the unique nature of Madagascar. Containing several endemic and threatened species, Ranomafana has been selected as one of UNESCO’s World Natural Heritage sites. However, due to strong human pressures the region immediately surroundings the protected area has severely degraded. Aims of this thesis were to inventory carabid fauna in RNP and evaluate their use as indicators of the environmental change. Carabid beetles were collected from protected area (secondary and primary forests) and from its degraded surrounding area. Collecting was mostly conducted by hand during years 2000-2005. Species compositions between the protected area and its surroundings were compared, and species habitat preferences and seasonal variations were studied. In total, 4498 individuals representing 127 carabid species (of which 38 are new species) were collected. Species compositions within and outside of the protected area were markedly different. Most of the species preferred forest as their primary habitat and were mainly collected from trees and bushes. Their value as indicators is based on their different habitat requirements and sensitivity to environmental variables. Some of the species were found only in the protected forest, some occupied also the degraded forests and some preferred open areas. Carabid fauna is very species rich in Ranomafana and there are still many species to be found. Most of the species are arboreal and probably cannot survive in the deforested areas outside the park. This is very likely also the case for other species. Establishment and continued protection of RNP is probably the only way to conserve this globally important area. However, new occupations and land use methods are urgently needed by the local people for improving their own lives while maintaining the forest intact.Miljoonien vuosien kuluessa maapallolle on kehittynyt ainutlaatuinen lajisto, josta vasta pieni osa tunnetaan. Ihmisen väestömäärän kasvaessa ja luonnonvarojen käytön lisääntyessä suuri osa näistä lajeista on uhattuna ja vaarassa kuolla sukupuuttoon. Erityisen suuressa vaarassa ovat trooppisten alueiden lajit, sillä ne ovat suurimmaksi osaksi kotoperäisiä eli niitä elää vain tietyllä, usein pienellä, alueella. Madagaskar on yksi luonnoltaan rikkaimmista maista ja siksi yksi tärkeimmistä suojelukohteista maapallolla. Ihmisen tulo saarelle aiheutti useiden lajien kuolemisen sukupuuttoon ja moni on tälläkin hetkellä uhattuna. Ranomafanan kansallispuisto perustettiin suojelemaan Madagaskarin ainutlaatuista luontoa ja kotoperäisiä lajeja. Poikkeuksellisen arvokkaana kohteena Ranomafana on yksi UNESCOn maailmanperintöluettelon suojelukohteista. Kansallispuiston ympärillä on kuitenkin paljon ihmisasutusta ja alueen metsiin kohdistuvat suuret käyttöpaineet. Tässä työssä tutkin erästä kovakuoriaisryhmää maakiitäjäisiä (Coleoptera, Carabidae), Ranomafanan alueella sekä luonnontilaisessa metsässä että alueilla, joissa ihmisen vaikutus on suuri. Pohjoisella pallonpuoliskolla maakiitäjäiset elävät nimensä mukaisesti maassa, mutta suurin osa löytämistäni lajeista löytyikin puista. Tropiikissa puiden pinnoilla elävät sammalet, liaanit ym. kasvillisuus muodostaa kokonaan uuden elinympäristön mahdollistaen pienelle alueelle suuren lajimäärän. Kerätyistä 127 maakiitäjäislajista 38 oli tieteelle uusia lajeja. Suurin osa kerätyistä lajeista eli vain metsissä, joten metsien häviäminen tuhoaa elinmahdollisuuden monilta lajeilta. Ranomafanan kansallispuiston suojelu on siten ensiarvoisen tärkeää. Yhtä tärkeää olisi myös löytää paikallisille asukkaille toimeentulomuotoja, joiden avulla köyhyys vähentyisi, mutta metsät säilyisivät tulevaisuudessakin

Beyond the project cycle: an evaluation of agroforestry adoption and diffusion over the medium term in a south Indian village

Few studies explicitly assess the temporal and spatial dynamics of agroforestry adoption occurring beyond the project cycle. Where ex-post evaluations are published, abandonment of introduced agroforestry after project cessation is often reported. This paper presents an analysis of agroforestry adoption in a poor, peri-urban village in semi-arid south India, where 97 % of initial adopters had retained their plots six to eight years after implementation. The intervention was facilitated by BAIF, an Indian non-governmental organisation specialising in natural resource management. The complex technological package promoted was known as �wadi� and comprised fruit trees planted in crop fields, with a boundary of multi-purpose trees and integrated soil and water conservation measures. Sixty four agroforestry plots belonging to 43 households were surveyed in 2010/11 and interviews were held with both adopting and non-adopting farmers. Beyond retention, a quarter of adopters had expanded the practice on to additional areas of land and some diffusion to initially non-adopting farmers had also occurred. Adopters were found to have modified the practice to suit their own objectives, capabilities and constraints, highlighting that adoption is more than a simple binary choice. The study demonstrates the importance of external support for adoption of agroforestry. The intervention was not, however, especially pro-poor with adoption occurring disproportionately among relatively wealthier households with larger landholdings. Where poorer households adopted, this tended to occur later. Participation was entirely voluntary and, by 2011, conversion of suitable farmland to agroforestry had reached 18 %; while beneficial to individual adopters, this patchy coverage arguably limits the potential for enhanced ecosystem service provision at landscape-scale

Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88% of the cases, with large effect sizes for 95% of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95% of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.Comment: in IEEE Transactions on Software Engineerin

Integrated models, frameworks and decision support tools to guide management and planning in Northern Australia. Final report

[Extract] There is a lot of interest in developing northern Australia while also caring for the unique Australian landscape (Commonwealth of Australia 2015). However, trying to decide how to develop and protect at the same time can be a challenge. There are many modelling tools available to inform these decisions, including integrated models, frameworks, and decision support tools, but there are so many different kinds that it’s difficult to determine which might be best suited to inform different decisions. To support planning and development decisions across northern Australia, this project aimed to create resources to help end-users (practitioners) to assess: 1. the availability and suitability of particular modelling tools; and 2. the feasibility of using, developing, and maintaining different types of modelling tools

Process monitoring and control using live cell imaging for the manufacturing of cell therapies

Regenerative medicine (RM) represents a promising enabling technology to revolutionize healthcare. This said there are still major gaps between the commercial promise and the reality of the cell therapy sector of regenerative medicine. There is consensus to develop high through-put, automated technologies for the manufacture of RM products. Imaging methods will have the capacity to contribute to this technological gap for cell therapies and are particularly attractive to provide non-destructive monitoring with high spatial and temporal resolution. This work applied an automated, non-invasive phase contrast imaging platform (Cell-IQ) to measure, analyse and ultimately quantify image derived metrics for human embryonic stem cells (hESCs) and haematopoietic stem cells (HSCs) as part of the colony forming unit (CFU) assay. This work has shown through thresholding and machine vision identification technology, imaging has the ability to improve the precision of current evaluation methods for cell culture, providing novel information regarding culture state and show image derived metrics to be predictive of future culture state. Building on this, differentiation through the addition of a growth factor cocktail highlighted how in-process monitoring enables protocol optimisation. After equilibrating the Cell-IQ incubator to a standard incubator, the progress of the CFU assay was monitored and image metrics representative of colony phenotype were analysed. Cell count, distance between cells and cell migration within individual colonies were identified to be informative and provide a degree of colony phenotype separation. Quantitative, novel, image derived metrics were identified that improve reliability through computer automation, cost by removing user verification and time by reducing the assay time from 14 days to 7 days. Non-invasive imaging provides a fantastic opportunity to create bespoke sampling frequencies to achieve desired precision for manufacturing cell therapies, this work has developed and shown improvement and a level of control to current culture process for ESCs and HSCs