Context: Software testing is expensive, labor intensive and consumes lot of time in a software development life cycle. There was always a need in software testing to decrease the testing time. This also resulted to focus on Automated Software Testing (AST), because using automated testing, with specific tools, this effort can be dramatically reduced and the costs related with testing can decrease [11]. Manual Testing (MT) requires lot of effort and hard work, if we measure in terms of person per month [11]. Automated Software testing helps to decrease the work load by giving some testing tasks to the computers. Computer systems are cheap, they are faster and don‘t get bored and can work continuously in the weekends. Due to this advantage many researches are working towards the Automation of software testing, which can help to complete the task in less testing time [10]. Objectives: The main aims of this thesis is to 1.) To systematically classify contributions within AST. 2.) To identify the different benefits and challenges of AST. 3.) To identify the whether the reported benefits and challenges found in the literature are prevalent in industry. Methods: To fulfill our aims and objectives, we used Systematic mapping research methodology to systematically classify contributions within AST. We also used SLR to identify the different benefits and challenges of AST. Finally, we performed web based survey to validate the finding of SLR. Results: After performing Systematic mapping, the main aspects within AST include purpose of automation, levels of testing, Technology used, different types of research types used and frequency of AST studies over the time. From Systematic literature review, we found the benefits and challenges of AST. The benefits of AST include higher product quality, less testing time, reliability, increase in confidence, reusability, less human effort, reduction of cost and increase in fault detection. The challenges include failure to achieve expected goals, difficulty in maintenance of test automation, Test automation needs more time to mature, false expectations and lack of skilled people for test automation tools. From web survey, it is observed that almost all the benefits and challenges are prevalent in industry. The benefits such as fault detection and confidence are in contrary to the results of SLR. The challenge about the appropriate test automation strategy has 24 % disagreement from the respondents and 30% uncertainty. The reason is that the automation strategy is totally dependent on the test manager of the project. When asked “Does automated software testing fully replace manual testing”, 80% disagree with this challenge. Conclusion: The classification of the AST studies using systematic mapping gives an overview of the work done in the area of AST and also helps to find research coverage in the area of AST. These results can be used by researchers to use the gaps found in the mapping studies to carry on future work. The results of SLR and web survey clearly show that the practitioners clearly realize the benefits and challenges of AST reported in the literature. / Mobile no: +46723069909

A modern truck contains a large number of functionalities implemented in its electronics system. Thus, testing all of these functions employs a considerable effort. The execution of tests against the system has been automated for a long time. Unfortunately, most of the test is still designed manually these days. This manual test design is sometimes not comprehensive enough to cover all possible scenarios within a complex system. At the moment, there is also a growing trend in the development process based on the model. Furthermore, model-based software can handle events and signal behaviour more robustly[1]. This thesis investigates the technique in model-based testing. The study evaluates the requirement modelling and automated abstract test generation of model-based testing over the existing testing method. A cause-effect graph is utilized for the modelling in Matlab Simulink tool with DesignVerifier feature. The case study is the truck dosing system in Scania. The results are the following. The temporal and static requirements modelling are capable of being modelled using the cause-effect graph in Matlab Simulink. Compared to the traditional method, the MBT method can achieve higher requirement coverage and more rigorous test with optimized test case generation. The MBT method also has a rapid test case generation time suitable for quick design iteration. However, the total test development time (including test case generation time) of using MBT is 12.5% higher than the manual method. Using a model-based platform like Matlab Simulink is recommended to assist the manual testing, not to replace the test flow entirely with the current research state. / En modern truck innehåller ett stort antal funktioner implementerade i dess elektroniksystem. Att testa alla dessa funktioner kräver därför en avsevärd ansträngning. Utförandet av tester mot systemet har varit automatiserat under lång tid. Tyvärr är det mesta av testet fortfarande utformat manuellt nu för tiden. Denna manuella testdesign är ibland inte tillräckligt omfattande för att täcka alla möjliga scenarier inom ett komplext system. För tillfället finns det också en växande trend i utvecklingsprocessen utifrån modellen. Dessutom kan modellbaserad programvara hantera händelser och signalbeteende mer robust[1]. Detta examensarbete undersöker tekniken i modellbaserad testning. Studien utvärderar kravmodellering och automatiserad abstrakt testgenerering av modellbaserad testning över den befintliga testmetoden. En cause-effect graph används för modelleringen i Matlab Simulink-verktyget med Design Verifier-funktionen. Fallstudien är lastbilens doseringssystem i Scania. Resultaten är följande. Den tidsmässiga och statiska kravmodelleringen kan modelleras med hjälp av cause-effect graph i Matlab Simulink. Jämfört med den traditionella metoden kan MBT-metoden uppnå högre kravtäckning och mer rigorösa test med optimerad testfallsgenerering. MBT-metoden har också en snabb genereringstid för testfall som är lämplig för snabb designiteration. Den totala testutvecklingstiden (inklusive genereringstid för testfall) för att använda MBT är 12,5% högre än den manuella metoden. Att använda en modellbaserad plattform som Matlab Simulink rekommenderas för att underlätta den manuella testningen, inte för att ersätta testflödet helt med det aktuella forskningsläget.

The software industry is expanding at a rapid rate. To keep up with the fast-growing and ever-changing technologies, it has become necessary to produce high-quality software in a short time and at an affordable cost. This research aims to demonstrate to Java developers the use of Automated Test Case Generation (ATCG) tools by presenting the tools that exist today, their usefulness, and their effectiveness. The main focus is on the automated testing tools for the Java industry, which can help developers achieve their goals faster and make better software. Moreover, the discussion covers the availability, features, prerequisites, effectiveness, and limitations of the automated testing tools. Among these tools, the most widely used are Evosuite, JUnit, TestNG, and Selenium. Each tool has its advantages and purpose. Furthermore, these ATCG-tools were compared to provide a clear picture to Java developers, answer the research questions, and show strengths and limitations of each selected tool. Results show that there is no single ultimate tool that can do all kinds of testing independently. It all depends on what the developer aims to achieve. If one tool is good at generating unit test cases for Java classes, another tool is good at testing the code security through penetration testing. Therefore, the Java developers may choose a tool/s based on their requirements. This study has revealed captivating findings regarding the ATCG-tools, which ought to be explored in the future.