Prioritization of Re-executable Test Cases of Activity Diagram in Regression Testing Using Model Based Environment

As we all know, software testing is of vital importance in software development life cycle (SDLC) to validate the new versions of the software and detection of faults. Regression Testing, however concentrates on generating test cases on changed part of the software to detect faults more earlier than any other testing practices. In case of model based testing approach, testing is performed using top-down method (black box method) and design models of the software, for example, UML diagrams. UML diagrams gives us requirement level representation of the software in graphical format which is now a days a standard used in software engineering. In our proposed approach, we have derived a new technique which has never been used before to prioritize the test cases in model based environment. In this technique, we have used activity diagram as an input to the system. Activity diagram is used basically because it gives us the complete flow of each and every activity involved in the system and represents its complete working. Activity diagram is further changed as the requirement changes, each time, when the changes happen, they are recorded and test cases are generated for the changed diagram, test cases are also generated for the original diagram. Test cases for both the diagrams are compared and classified as re-usable and re-executable test cases. Re-usable test cases are those that remain unchanged during requirement changes and re-executable test cases belong to the changed part of the diagram. Then re-executable test cases are prioritized using one heuristic algorithm based on ACT(Activity Connector) table. Now, the question is why to prioritize only the re-executable test cases. Because, any how we have to execute re-usable test cases, as they remain same for both the versions of the diagram and are already tested when original diagram was made. But, re-executable test cases are never been tested and may detect faults in the modified design quickly and by prioritizing them we can also reduce the execution time of the test cases which will give us effective testing performance and will evolve a better new version of the software. All the existing prioritization techniques are either code based or are using various tool supports. Code based techniques are too complex and tedious because for a small change in code, we need to test whole application repeatedly. And in case of tool support, we have multiple assumptions and constraints to be followed. This proposed technique will surely give better results and as the type of technique has never been used before will also prove very effective. DOI: 10.17762/ijritcc2321-8169.15077

Vendor system integration testing on mobile point-of-sales system

The purpose of this report is to study and identify the suitable test strategy and software testing methodology for Mobile Point-of-Sales (MBPOS) system, develop the test plan for Vendor System Integration Test (VSIT), design the test cases by implementing all MBPOS requirements and perform the VSIT and report the result of testing. The Software Test Plan, Software Test Description and Software Test Report are the deliverables of this report. The issue of this report is MBPOS system does not implement the field validations such as the field length and the maximum and minimum value needed for each field during the VSIT phase. So, this report is important which the deliverables of this report implementing all the requirements of MBPOS system during testing to avoid or minimize any defect raised by the user during the following phases. The software testing methodology used in this report is systematically covers Planning, Executive, Monitoring and Closing phases. In future, hope that MBPOS can implement the Requirement Traceability Matrix to avoid any requirements miss out in the test cases during the testing and become the most quality system produces from this organization

Regression Test Selection by Exclusion

This thesis addresses the research in the area of regression testing. Software systems change and evolve over time. Each time a system is changed regression tests have to be run to validate these changes. An important issue in regression testing is how to minimise reuse the existing test cases of original program for modied program. One of the techniques to tackle this issue is called regression test selection technique. The aim of this research is to signicantly reduce the number of test cases that need to be run after changes have been made. Specically, this thesis focuses on developing a model for regression test selection using the decomposition slicing technique. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of the system. The model of regression test selection based on decomposition slicing and exclusion of test cases was developed in this thesis. The model is called Regression Test Selection by Exclusion (ReTSE) and has four main phases. They are Program Analysis, Comparison, Exclusion and Optimisation phases. The validity of the ReTSE model is explored through the application of a number of case studies. The case studies tackle all types of modication such as change, delete and add statements. The case studies have covered a single and combination types of modication at a time. The application of the proposed model has shown that signicant reductions in the number of test cases can be achieved. The evaluation of the model based on an existing framework and comparison with another model also has shown promising results. The case studies have limited themselves to relatively small programs and the next step is to apply the model to larger systems with more complex changes to ascertain if it scales up. While some parts of the model have been automated tools will be required for the rest when carrying out the larger case studies

Effizienter Regressionstest von E/E-Systemen nach ISO 26262

Selektive Regressionstestmethodiken analysieren auf Basis einer auf eine Systemdarstellung abgebildeten Modifikation, welche Testfälle für eine systematische Überprüfung der Änderung selbst sowie aller potentiellen durch eine mögliche Fehlwirkung betroffenen Teilbereiche des Systems notwendig sind. Im Rahmen dieser Arbeit wird erstmals eine effiziente und spezifikationsbasierte Regressionstestmethodik nach ISO 26262 für die E/E-Systemebene entwickelt

Black-Box Testfall-Selektion und -Priorisierung für Software-Varianten und -Versionen

Software testing is a fundamental task in software quality assurance. Especially when dealing with several product variants or software versions under test, testing everything for each variant and version is infeasible due to limited testing resources. To cope with increasing complexity both in time (i.e., versions) and space (i.e., variants), new techniques have to be developed to focus on the most important parts for testing. In the past, regression testing techniques such as test case selection and prioritization have emerged to tackle these issues for single-software systems. However, testing of variants and versions is still a challenging task, especially when no source code is available. Most existing regression testing techniques analyze source code to identify important changes to be retested, i.e., they are likely to reveal a failure. To this end, this thesis contributes different techniques for both, variants and versions, to allow more efficient and effective testing in difficult black-box scenarios by identifying important test cases to be re-executed. Four major contributions in software testing are made. (1) We propose a test case prioritization framework for software product lines based on delta-oriented test models to reduce the redundancy in testing between different product variants.(2) We introduce a risk-based testing technique for software product lines. Our semi-automatic test case prioritization approach is able to compute risk values for test model elements and scales with large numbers of product variants. (3) For black-box software versions, we provide a test case selection technique based on genetic algorithms. In particular, seven different black-box selection objectives are defined, thus, we perform a multi-objective test case selection finding Pareto optimal test sets to reduce the testing effort. (4) We propose a novel test case prioritization technique based on supervised machine learning. It is able to imitate decisions made by experts based on different features, such as natural language test case descriptions and black-box meta-data. All of these techniques have been evaluated using the Body Comfort System case study. For testing of software versions, we also assesses our testing techniques using an industrial system. Our evaluation results indicate that our black-box testing approaches for software variants and versions are able to successfully reduce testing effort compared to existing techniques.Testen ist eine fundamentale Aufgabe zur Qualitätssicherung von modernen Softwaresystemen. Mangels limitierter Ressourcen ist das Testen von vielen Produktvarianten oder Versionen sehr herausfordernd und das wiederholte Ausführen aller Testfälle nicht wirtschaftlich. Um mit der Raum- (Varianten) und Zeitdimension (Versionen) in der Entwicklung umzugehen, wurden in der Vergangenheit verschiedene Testansätze entwickelt. Es existieren jedoch nach wie vor große Herausforderungen, welche es zu lösen gilt. Dies ist vor allem der Fall, wenn der Quellcode der getesteten Softwaresysteme unbekannt ist. Das Testen von Black-Box-Systemen erschwert die Identifikation von zu testenden Unterschieden zu vorher getesteten Varianten oder Versionen. In der Literatur finden sich wenige Ansätze, welche versuchen diese Herausforderungen zu lösen. Daher werden in dieser Dissertation neue Ansätze entwickelt und vorgestellt, welche beim Black-Box Testen von Software-Varianten und -Versionen helfen, wichtige Testfälle zur erneuten Ausführung zu identifizieren. Dies erspart die Ausführung von Testfällen, welche weder neues Verhalten testen noch mit hoher Wahrscheinlichkeit neue Fehler zu finden. Insgesamt leistet diese Dissertation die folgenden vier wissenschaftlichen Beiträge: (1) Ein modell-basiertes Framework zur Definition von Testfallpriorisierungsfunktionen für variantenreiche Systeme. Das Framework ermöglicht eine flexible Priorisierung von Testfällen für individuelle Produktvarianten. (2) Einen risiko-basierten Testfallpriorisierungsansatz für variantenreiche Systeme. Das Verfahren ermöglicht eine semi-automatisierte Berechnung von Risikowerten für Elemente von Produktvarianten und skaliert mit großen Produktzahlen. (3) Ein multi-kriterielles Testfallselektionsverfahren für den Regressionstest von Black-Box Software-Versionen. Es werden Black-Box Testkriterien aufgestellt und mittels eines genetischen Algorithmus optimiert um Pareto-optimale Testsets zu berechnen. (4) Ein Testfallpriorisierungsverfahren für Black-Box Regressionstests mit Hilfe von Machine Learning. Der verwendete Algorithmus imitiert Entscheidungen von Testexperten um wichtige Testfälle zu identifizieren. Diese Ansätze wurden alle mit Hilfe von Fallstudien evaluiert. Die resultierenden Ergebnisse zeigen, dass die Ansätze die gewünschten Ziele erreichen und helfen, wichtige Testfälle effektiv zu identifizieren. Insgesamt wird der Testaufwand im Vergleich zu existierenden Techniken verringert