Test Case Prioritization for Software Product Line: A Systematic Mapping Study

Combinatorial explosion remains a common issue in testing. Due to the vast number of product variants, the number of test cases required for comprehensive coverage has significantly increased. One of the techniques to efficiently tackle this problem is prioritizing the test suites using a regression testing method. However, there is a lack of comprehensive reviews focusing on test case prioritization in SPLs. To address this research gap, this paper proposed a systematic mapping study to observe the extent of test case prioritization usage in Software Product Line Testing. The study aims to classify various aspects of SPL-TCP (Software Product Line – Test Case Prioritization), including methods, criteria, measurements, constraints, empirical studies, and domains. Over the last ten years, a thorough investigation uncovered twenty-four primary studies, consisting of 12 journal articles and 12 conference papers, all related to Test Case Prioritization for SPLs. This systematic mapping study presents a comprehensive classification of the different approaches to test case prioritization for Software Product Lines. This classification can be valuable in identifying the most suitable strategies to address specific challenges and serves as a guide for future research works. In conclusion, this mapping study systematically classifies different approaches to test case prioritization in Software Product Lines. The results of this study can serve as a valuable resource for addressing challenges in SPL testing and provide insights for future research

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

Potential Errors and Test Assessment in Software Product Line Engineering

Software product lines (SPL) are a method for the development of variant-rich software systems. Compared to non-variable systems, testing SPLs is extensive due to an increasingly amount of possible products. Different approaches exist for testing SPLs, but there is less research for assessing the quality of these tests by means of error detection capability. Such test assessment is based on error injection into correct version of the system under test. However to our knowledge, potential errors in SPL engineering have never been systematically identified before. This article presents an overview over existing paradigms for specifying software product lines and the errors that can occur during the respective specification processes. For assessment of test quality, we leverage mutation testing techniques to SPL engineering and implement the identified errors as mutation operators. This allows us to run existing tests against defective products for the purpose of test assessment. From the results, we draw conclusions about the error-proneness of the surveyed SPL design paradigms and how quality of SPL tests can be improved.Comment: In Proceedings MBT 2015, arXiv:1504.0192

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

Spectrum-based fault localization in software product lines

Context: Software Product Line (SPL) testing is challenging mainly due to the potentially huge number of products under test. Most of the research on this field focuses on making testing affordable by selecting a representative subset of products to be tested. However, once the tests are executed and some failures revealed, debugging is a cumbersome and time consuming task due to difficulty to localize and isolate the faulty features in the SPL. Objective: This paper presents a debugging approach for the localization of bugs in SPLs. Method: The proposed approach works in two steps. First, the features of the SPL are ranked according to their suspiciousness (i.e., likelihood of being faulty) using spectrum-based localization techniques. Then, a novel fault isolation approach is used to generate valid products of minimum size containing the most suspicious features, helping to isolate the cause of failures. Results: For the evaluation of our approach, we compared ten suspiciousness techniques on nine SPLs of different sizes. The results reveal that three of the techniques (Tarantula, Kulcynski2 and Ample2) stand out over the rest, showing a stable performance with different types of faults and product suite sizes. By using these metrics, faults were localized by examining between 0.1% and 14.4% of the feature sets. Conclusion: Our results show that the proposed approach is effective at locating bugs in SPLs, serving as a helpful complement for the numerous approaches for testing SPLs.Ministerio de Economía y Competitividad TIN2015-70560-R (BELI)Junta de Andalucía P12-TIC-1867 (COPAS

A dissimilarity with dice-jaro-winkler test case prioritization approach for model- based testing in software product line

The effectiveness of testing in Model-based Testing (MBT) for Software Product Line (SPL) can be achieved by considering fault detection in test case. The lack of fault consideration caused test case in test suite to be listed randomly. Test Case Prioritization (TCP) is one of regression techniques that is adaptively capable to detect faults as early as possible by reordering test cases based on fault detection rate. However, there is a lack of studies that measured faults in MBT for SPL. This paper proposes a Test Case Prioritization (TCP) approach based on dissimilarity and string based distance called Last Minimal for Local Maximal Distance (LM-LMD) with Dice-Jaro-Winkler Dissimilarity. LM-LMD with Dice-Jaro-Winkler Dissimilarity adopts Local Maximum Distance as the prioritization algorithm and Dice-Jaro-Winkler similarity measure to evaluate distance among test cases. This work is based on the test case generated from statechart in Software Product Line (SPL) domain context. Our results are promising as LM-LMD with Dice-Jaro-Winkler Dissimilarity outperformed the original Local Maximum Distance, Global Maximum Distance and Enhanced All-yes Configuration algorithm in terms of Average Fault Detection Rate (APFD) and average prioritization time