Studien undersöker de automatiserade testverktygen: Ghost Inspector och Selenium. En limiterad litteraturstudie och två fallstudier görs, för att undersöka och utvärdera testverktygen. Detta för att kunna tillgodose riktlinjer som är användbara, vid val av ett verktyg för att utföra regressionstestning på webbapplikationer.IStone använder Ghost Inspector som verktyg vid regressionstest på webb applikationer, eftersom det ger dem en effektiv konfigurering och underhålls- process. Selenium kräver mer ansträngning för konfigurering och underhåll, eftersom verktygen inte är en tjänst. Miljön i Ghost Inspector underlättar för iStone att exekvera test kontinuerligt och förser projektteamet med det senaste testresultatet. / The study evaluates the automatic testing tools: Ghost Inspector and Selenium. and focuses on comparing them in terms of the set up and maintenance processes. The study includes a limited literature study and two case studies. The purpose is to evaluate the tools and provide guidelines for choosing a regression testing tool when testing the functionality of web applications.IStone use Ghost Inspector as regressions testing tool on web applications, since it offers an effective set up and maintenance process. Selenium requires more effort to set up and maintain, since Selenium is not a service. The Ghost Inspector environment comforts iStone with the ability of executing test continuously and supplies the latest test results to the project team.

The constant modification of software during its life cycle poses many challenges for developers and testers because changes might not behave as expected or may introduce erroneous side effects. For those reasons, it is of critical importance to analyze, test, and validate software every time it changes. The most common method for validating modified software is regression testing, which identifies differences in the behavior of software caused by changes and determines the correctness of those differences. Most research to this date has focused on the efficiency of regression testing by selecting and prioritizing existing test cases affected by changes. However, little attention has been given to finding whether the test suite adequately tests the effects of changes (i.e., behavior differences in the modified software) and which of those effects are missed during testing. In practice, it is necessary to augment the test suite to exercise the untested effects. The thesis of this research is that the effects of changes on software behavior can be computed with enough precision to help testers analyze the consequences of changes and augment test suites effectively. To demonstrate this thesis, this dissertation uses novel insights to develop a fundamental understanding of how changes affect the behavior of software. Based on these foundations, the dissertation defines and studies new techniques that detect these effects in cost-effective ways. These techniques support test-suite augmentation by (1) identifying the effects of individual changes that should be tested, (2) identifying the combined effects of multiple changes that occur during testing, and (3) optimizing the computation of these effects.

For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.

We live in a society where automatization is becoming more common, whether it be cars or artificial intelligence. Software needs to be updated using patches, however, these patches have the possibility of breaking components. This study takes such a patch in the context of Ericsson, identifies what needs to be tested, investigates whether the tests can be automated and assesses how maintainable they are. Interviews were used for the identification of system and software parts in need of testing. Then tests were implemented in an automated test suite to test functionality of either a system or software. The goal was to reduce time of troubleshooting for employees without interrupting sessions for users as well as set up a working test suite. When the automated testing is completed and implemented in the test suite, the study is concluded by measuring the maintainability of the scripts using both metrics and human assessment through interviews. The result showed the testing suite proved maintainable, both from the metric point of view and from human assessment.

This thesis presents an in-depth exploration of regression testing metrics and their relationship with software quality, based on a qualitative case study within a global technology organization. By applying a thematic analysis to empirical data collected through interviews and a survey with the organisations Quality and Assurance team, this study identifies three primary themes: foundation of credibility, organic test maintenance, and defect management. The research highlights key metrics underlying each theme, including requirement coverage, code coverage, historical pass-rate, test flakiness, and code complexity. These metrics, especially when implemented in the early stages of a project, emerged as indicative of the overall software quality and stability. Through this research, a deeper understanding of regression testing metrics and their critical role in increasing the software quality is achieved. However, it is apparent that further research is necessary, including broader case studies, investigation into the application of machine learning algorithms, and exploration of additional metrics, to fully understand their universal applicability. The insights in the findings from this thesis are aimed at contributing to the field of software quality assurance academically and also provide actionable insights to industry professionals in the field of software quality. / Denna avhandling presenterar en djupgående utforskning av regressionstestnings metriker och deras relation till programvarukvalitet, baserat på en kvalitativ fallstudie hos en global organisation aktiv inom teknologi. Genom att tillämpa en tematisk analys på empirisk data insamlad genom intervjuer och en enkätundersökning, identifierar denna studie tre huvudteman: testtillförlitlighet, testunderhåll och testprestanda. Forskningen lyfter fram nyckelmetriker som ligger till grund för varje tema. Dessa inkluderar ”Requirement coverage”, ”Code coverage, ”Historic pass-rate” , ”Test flakiness” och ”Code complexity”. Dessa metriker, särskilt när de implementeras i de tidiga stadierna av ett projekt, framträdde som indikativa för den övergripande programvarukvaliteten och programvarustabiliteten. Genom denna forskning uppnås en djupare förståelse för regressionstestningens metriker och deras kritiska roll i en ökad programvarukvalitet. Det är dock uppenbart att ytterligare forskning behövs, inklusive bredare fallstudier, undersökning av tillämpningen av maskininlärningsalgoritmer och utforskning av ytterligare metriker, för att fullt ut förstå deras universella användningsområden. Insikterna i resultaten från denna avhandling syftar till att bidra till fältet för regressionstestning akademiskt och även ge praktiska insikter till yrkesverksamma inom programvarukvalitet.

The main goal of this thesis is to describe the implementation of continuous integration into the k-Wave project. The thesis focuses primarily on the version written in the C/C++ language with the usage of the OpenMP library which typically runs on supercomputers. Accordingly, many of popular workflows and approaches ought to be adapted, a few more created. The outcome of the thesis is a complete solution with real and practical usage. The author provides design, tools selection, runtime environment administration and configuration for each one of the used services. Software implementation of the basic framework is used in order to utilize running tests on the supercomputers. Furthermore, the implementation of chosen types of regression and unit tests are performed. Realisation is based on Gitlab and Jenkis services that are running on separated Docker containers.