Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. In Proceedings of 26th International Symposium on Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC

A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Industrially Applicable System Regression Test Prioritization in Production Automation

When changes are performed on an automated production system (aPS), new faults can be accidentally introduced in the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and on-site in a very uncomfortable environment. Until now, there is no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.Comment: 13 pages, https://ieeexplore.ieee.org/abstract/document/8320514

Modellbasiertes Regressionstesten von Varianten und Variantenversionen

The quality assurance of software product lines (SPL) achieved via testing is a crucial and challenging activity of SPL engineering. In general, the application of single-software testing techniques for SPL testing is not practical as it leads to the individual testing of a potentially vast number of variants. Testing each variant in isolation further results in redundant testing processes by means of redundant test-case executions due to the shared commonality. Existing techniques for SPL testing cope with those challenges, e.g., by identifying samples of variants to be tested. However, each variant is still tested separately without taking the explicit knowledge about the shared commonality and variability into account to reduce the overall testing effort. Furthermore, due to the increasing longevity of software systems, their development has to face software evolution. Hence, quality assurance has also to be ensured after SPL evolution by testing respective versions of variants. In this thesis, we tackle the challenges of testing redundancy as well as evolution by proposing a framework for model-based regression testing of evolving SPLs. The framework facilitates efficient incremental testing of variants and versions of variants by exploiting the commonality and reuse potential of test artifacts and test results. Our contribution is divided into three parts. First, we propose a test-modeling formalism capturing the variability and version information of evolving SPLs in an integrated fashion. The formalism builds the basis for automatic derivation of reusable test cases and for the application of change impact analysis to guide retest test selection. Second, we introduce two techniques for incremental change impact analysis to identify (1) changing execution dependencies to be retested between subsequently tested variants and versions of variants, and (2) the impact of an evolution step to the variant set in terms of modified, new and unchanged versions of variants. Third, we define a coverage-driven retest test selection based on a new retest coverage criterion that incorporates the results of the change impact analysis. The retest test selection facilitates the reduction of redundantly executed test cases during incremental testing of variants and versions of variants. The framework is prototypically implemented and evaluated by means of three evolving SPLs showing that it achieves a reduction of the overall effort for testing evolving SPLs.Testen ist ein wichtiger Bestandteil der Entwicklung von Softwareproduktlinien (SPL). Aufgrund der potentiell sehr großen Anzahl an Varianten einer SPL ist deren individueller Test im Allgemeinen nicht praktikabel und resultiert zudem in redundanten Testfallausführungen, die durch die Gemeinsamkeiten zwischen Varianten entstehen. Existierende SPL-Testansätze adressieren diese Herausforderungen z.B. durch die Reduktion der Anzahl an zu testenden Varianten. Jedoch wird weiterhin jede Variante unabhängig getestet, ohne dabei das Wissen über Gemeinsamkeiten und Variabilität auszunutzen, um den Testaufwand zu reduzieren. Des Weiteren muss sich die SPL-Entwicklung mit der Evolution von Software auseinandersetzen. Dies birgt weitere Herausforderungen für das SPL-Testen, da nicht nur für Varianten sondern auch für ihre Versionen die Qualität sichergestellt werden muss. In dieser Arbeit stellen wir ein Framework für das modellbasierte Regressionstesten von evolvierenden SPL vor, das die Herausforderungen des redundanten Testens und der Software-Evolution adressiert. Das Framework vereint Testmodellierung, Änderungsauswirkungsanalyse und automatische Testfallselektion, um einen inkrementellen Testprozess zu definieren, der Varianten und Variantenversionen unter Ausnutzung des Wissens über gemeinsame Funktionalität und dem Wiederverwendungspotential von Testartefakten und -resultaten effizient testet. Für die Testmodellierung entwickeln wir einen Ansatz, der Variabilitäts- sowie Versionsinformation von evolvierenden SPL gleichermaßen für die Modellierung einbezieht. Für die Änderungsauswirkungsanalyse definieren wir zwei Techniken, um zum einen Änderungen in Ausführungsabhängigkeiten zwischen zu testenden Varianten und ihren Versionen zu identifizieren und zum anderen die Auswirkungen eines Evolutionsschrittes auf die Variantenmenge zu bestimmen und zu klassifizieren. Für die Testfallselektion schlagen wir ein Abdeckungskriterium vor, das die Resultate der Auswirkungsanalyse einbezieht, um automatisierte Entscheidungen über einen Wiederholungstest von wiederverwendbaren Testfällen durchzuführen. Die abdeckungsgetriebene Testfallselektion ermöglicht somit die Reduktion der redundanten Testfallausführungen während des inkrementellen Testens von Varianten und Variantenversionen. Das Framework ist prototypisch implementiert und anhand von drei evolvierenden SPL evaluiert. Die Resultate zeigen, dass eine Aufwandsreduktion für das Testen evolvierender SPL erreicht wird

SOFTWARE UNDER TEST DALAM PENELITIAN SOFTWARE TESTING: SEBUAH REVIEW

Software under Test (SUT) is an essential aspect of software testing research activities. Preparation of the SUT is not simple. It requires accuracy, completeness and will affect the quality of the research conducted. Currently, there are several ways to utilize an SUT in software testing research: building an own SUT, utilization of open source to build an SUT, and SUT from the repository utilization. This article discusses the results of SUT identification in many software testing studies. The research is conducted in a systematic literature review (SLR) using the Kitchenham protocol. The review process is carried out on 86 articles published in 2017-2020. The article was selected after two selection stages: the Inclusion and Exclusion Criteria and the quality assessment. The study results show that the trend of using open source is very dominant. Some researchers use open source as the basis for developing SUT, while others use SUT from a repository that provides ready-to-use SUT. In this context, utilization of the SUT from the software infrastructure repository (SIR) and Defect4J are the most significant choice of researchers

Toward collisions produced in requirements rankings: A qualitative approach and experimental study

Requirements prioritization is an important issue that determines the way requirements are selected and processed in software projects. There already exist specific methods to classify and prioritize requirements, most of them based on quantitative measures. However, most of existing approaches do not consider collisions, which are an important concern in large-scale requirements sets and, more specifically, in agile development processes where requirements have to be uniquely selected for each software increment. In this paper, we propose QMPSR (Qualitative Method for Prioritizing Software Requirements), an approach that features the prioritization of requirements by considering qualitative elements that are related to the project's priorities. Our approach highlights a prioritization method that has proven to reduce collisions in software requirements rankings. Furthermore, QMPSR improves accuracy in classification when facing large-scale requirements sets, featuring no scalability problems as the number of requirements increases. We formally introduce QMPSR and then define prioritization effort and collision metrics to carry out comprehensive experiments involving different sets of requirements, comparing our approach with well-known existing prioritization methods. The experiments have provided satisfactory results, overcoming existing approaches and ensuring scalabilityThis work was partially supported by the Spanish Government [grant number RTI2018-095255-B-I00 ] and the Madrid Research Council [grant number P2018/TCS-4314

Software Testing Methodologies: A Information Review

In today’s Complex era, the need for simplest software application has increased massively. The quality of such a  handy application along with adequate testing is the biggest challenge one can face. Software Testing is an integral part of any software development which has to be followed right from the sapling phase of development. This paper focuses on testing methodologies which are used prior along with testing techniques for quality assurance and best of the quality