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See detailOn the Maintenance of System User Interactive Tests
Rwemalika, Renaud UL

Doctoral thesis (2021)

Many companies rely on software testing to verify that their software products meet their requirements. As such, software testing is a keystone of the quality process. It offers means to assess that both ... [more ▼]

Many companies rely on software testing to verify that their software products meet their requirements. As such, software testing is a keystone of the quality process. It offers means to assess that both functional and technical requirements are met. However, scaling the number of tests while preserving their quality poses challenges, just as in any software artifact. This is particularly true in the case of complex tests interacting with the SUT through its Graphical User Interface (GUI), i.e. System User Interactive Test (SUIT). The problem becomes challenging because as the System Under Test (SUT) evolves, the SUIT suites need to adapt and conform to the evolved software. Indeed, because the user interface is a part of the system that tends to undergo rapid evolutions, SUITs are particularly prompt to break. As the first contribution of this dissertation, we aim to demonstrate the problem of test maintenance and overall improve the understanding of SUIT scripts evolution. To that end, we identify, collect and analyze test code changes across the evolution of an industrial test suite. We show that the problem of test maintenance is largely due to test fragility (most commonly performed changes are due to locator and synchronization issues) and bad practices (over 30% of keywords are duplicated). To further investigate the question of bad test code practices such as test clones, we perform a multivocal study to identify which bad practices, i.e. SUIT smells, are already studied in both industry and academia. This process yields a catalog of 35 test code smells. For 16 of them, we derive metrics to analyze their diffusion across tests as well as potential refactoring actions removing the test code smells. Through an empirical study including both industrial and open-source test suites, we show that test code smells are largely present in SUITs, potentially contributing to SUIT fragility and hindering the maintenance process. Interestingly, refactoring actions remain rare during the lifespan of the tests. Yet, symptoms tend to disappear as old tests are discarded and new ones are introduced. However, during the analysis of SUIT smells, we observe that bad practices impacting locators do not appear often in the test code. This observation contrasts with the analysis of the evolution of the SUIT presented in our first empirical study. This apparent contradiction arises from the limitation of property-based locators which rely on the structure of the representation layer of the SUT. Indeed, such approaches are sensitive to internal iso-functional changes occurring during the normal evolution of the SUT. To account for this limitation, we introduce a new way of expressing locators, HPath. Instead of relying on the internal representation of the presentation layer, HPath relies on the rendered characteristics of the GUI. Our results suggest that despite what is presented in the literature on locators, expressions relying on a smaller number of GUI elements to fully qualify a target do not necessarily lead to more robust locators. On the contrary, the choice of the GUI element properties seems to play a stronger role in the robustness to change. Overall, this dissertation provides insights into how SUITs evolve and shows that SUIT fragility plays a major role in the associated maintenance effort. It also proposes techniques that effectively facilitate SUIT maintenance with the early detection of sub-optimal patterns and the introduction of a locator representation more robust to SUT evolution. [less ▲]

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See detailData-driven simulation and optimization for covid-19 exit strategies
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

in Ghamizi, Salah; Rwemalika, Renaud; Cordy, Maxime (Eds.) et al Data-driven simulation and optimization for covid-19 exit strategies (2020, August)

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive ... [more ▼]

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers.Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R² score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies. [less ▲]

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See detailPandemic Simulation and Forecasting of exit strategies:Convergence of Machine Learning and EpidemiologicalModels
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

Report (2020)

The COVID-19 pandemic has created a public health emergency unprecedented in this century. The lack ofaccurate knowledge regarding the outcomes of the virus has made it challenging for policymakers to ... [more ▼]

The COVID-19 pandemic has created a public health emergency unprecedented in this century. The lack ofaccurate knowledge regarding the outcomes of the virus has made it challenging for policymakers to decideon appropriate countermeasures to mitigate its impact on society, in particular the public health and the veryhealthcare system.While the mitigation strategies (including the lockdown) are getting lifted, understanding the current im-pacts of the outbreak remains challenging. This impedes any analysis and scheduling of measures requiredfor the different countries to recover from the pandemic without risking a new outbreak.Therefore, we propose a novel approach to build realistic data-driven pandemic simulation and forecastingmodels to support policymakers. Our models allow the investigation of mitigation/recovery measures andtheir impact. Thereby, they enable appropriate planning of those measures, with the aim to optimize theirsocietal benefits.Our approach relies on a combination of machine learning and classical epidemiological models, circum-venting the respective limitations of these techniques to allow a policy-making based on established knowl-edge, yet driven by factual data, and tailored to each country’s specific context. [less ▲]

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See detailAn industrial study on the differences between pre-release and post-release bugs
Rwemalika, Renaud UL; Kintis, Marinos; Papadakis, Mike UL et al

in Proceedings of 35th IEEE International Conference on Software Maintenance and Evolution (2019, September 30)

Software bugs constitute a frequent and common issue of software development. To deal with this problem, modern software development methodologies introduce dedicated quality assurance procedures. At the ... [more ▼]

Software bugs constitute a frequent and common issue of software development. To deal with this problem, modern software development methodologies introduce dedicated quality assurance procedures. At the same time researchers aim at developing techniques capable of supporting the early discovery and fix of bugs. One important factor that guides such research attempts is the characteristics of software bugs and bug fixes. In this paper, we present an industrial study on the characteristics and differences between pre-release bugs, i.e. bugs detected during software development, and post-release bugs, i.e. bugs that escaped to production. Understanding such differences is of paramount importance as it will improve our understanding on the testing and debugging support that practitioners require from the research community, on the validity of the assumptions of several research techniques, and, most importantly, on the reasons why bugs escape to production. To this end, we analyze 37 industrial projects from our industrial partner and document the differences between pre-release bugs and post-release bugs. Our findings suggest that post-release bugs are more complex to fix, requiring developers to modify several source code files, written in different programming languages, and configuration files, as well. We also find that approximately 82% of the post-release bugs involve code additions and can be characterized as "omission" bugs. Finally, we conclude the paper with a discussion on the implications of our study and provide guidance to future research directions. [less ▲]

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See detailOn the Evolution of Keyword-Driven Test Suites
Rwemalika, Renaud UL; Kintis, Marinos UL; Papadakis, Mike UL et al

in 12th IEEE International Conference on Software Testing, Verification and Validation (2019)

Many companies rely on software testing to verify that their software products meet their requirements. However, test quality and, in particular, the quality of end-to-end testing is relatively hard to ... [more ▼]

Many companies rely on software testing to verify that their software products meet their requirements. However, test quality and, in particular, the quality of end-to-end testing is relatively hard to achieve. The problem becomes challenging when software evolves, as end-to-end test suites need to adapt and conform to the evolved software. Unfortunately, end-to-end tests are particularly fragile as any change in the application interface, e.g., application flow, location or name of graphical user interface elements, necessitates a change in the tests. This paper presents an industrial case study on the evolution of Keyword-Driven test suites, also known as Keyword-Driven Testing (KDT). Our aim is to demonstrate the problem of test maintenance, identify the benefits of Keyword-Driven Testing and overall improve the understanding of test code evolution (at the acceptance testing level). This information will support the development of automatic techniques, such as test refactoring and repair, and will motivate future research. To this end, we identify, collect and analyze test code changes across the evolution of industrial KDT test suites for a period of eight months. We show that the problem of test maintenance is largely due to test fragility (most commonly-performed changes are due to locator and synchronization issues) and test clones (over 30% of keywords are duplicated). We also show that the better test design of KDT test suites has the potential for drastically reducing (approximately 70%) the number of test code changes required to support software evolution. To further validate our results, we interview testers from BGL BNP Paribas and report their perceptions on the advantages and challenges of keyword-driven testing. [less ▲]

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See detailThe Importance of Accounting for Real-World Labelling When Predicting Software Vulnerabilities
Jimenez, Matthieu; Rwemalika, Renaud UL; Papadakis, Mike UL et al

in Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (2019)

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See detailAn Empirical Study on Vulnerability Prediction of Open-Source Software Releases
Jimenez, Matthieu; Rwemalika, Renaud UL; Papadakis, Mike UL et al

in Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (2019)

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See detailCan we automate away the main challenges of end-to-end testing?
Rwemalika, Renaud UL; Kintis, Marinos UL; Papadakis, Mike UL et al

Scientific Conference (2018, December 11)

Agile methodologies enable companies to drastically increase software release pace and reduce time-to-market. In a rapidly changing environment, testing becomes a cornerstone of the software development ... [more ▼]

Agile methodologies enable companies to drastically increase software release pace and reduce time-to-market. In a rapidly changing environment, testing becomes a cornerstone of the software development process, guarding the system code base from the insertion of faults. To cater for this, many companies are migrating manual end-to-end tests to automated ones. This migration introduces several challenges to the practitioners. These challenges relate to difficulties in the creation of the automated tests, their maintenance and the evolution of the test code base. In this position paper, we discuss our preliminary results on such challenges and present two potential solutions to these problems, focusing on keyword-driven end-to-end tests. Our solutions leverage existing software artifacts, namely the test suite and an automatically-created model of the system under test, to support the evolution of keyword-driven test suites. [less ▲]

Detailed reference viewed: 293 (27 UL)