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

Cure kinetics and thermodynamics of polyurethane network formation based on castor oil based polyester polyol and 4,4’-diphenyl methane diisocyanate

336-342In this work, isothermal curing kinetics of a non-catalysed and non-blown reaction between castor oil based polyester polyol and polymeric 4, 4’-diphenyl methane diisocyanate (MDI) has been investigated using Differential Scanning calorimeter (DSC) and viscosity build up studies. Several phenomenological models like Ozawa, Kissinger and Kissinger- Akahira-Sunose (KAS) isoconversional methods has been adopted to study polymerisation kinetics through DSC. DSC cure kinetics is studied at different heating rates (5°C/min, 10°C/min, 15°C/min and 20°C/min). Viscosity build up studies are also done for evaluating the kinetic parameters. These studies have been conducted for an isocyanate index [NCO equivalents/OH equivalents] of 1:1 and 1:2. Dynamic viscosity is measured as a function of time and rate constant and activation energy of the curing system is evaluated. Activation energy obtained for 1:1 index through Ozawa and Kissinger methods is found to be in the range of 70kJ/mol and for 1:2 index it is found to be in the range of 50kJ/mol. Thermodynamic parameters like Gibb’s free energy (Activation Free Energy), activation enthalpy and activation entropy of the polymerisation kinetics is calculated using Wynne-Jones-Eyring-Evans Theory

Conditional transition systems with upgrades

We introduce a variant of transition systems, where activation of transitions depends on conditions of the environment and upgrades during runtime potentially create additional transitions. Using a cornerstone result in lattice theory, we show that such transition systems can be modelled in two ways: as conditional transition systems (CTS) with a partial order on conditions, or as lattice transition systems (LaTS), where transitions are labelled with the elements from a distributive lattice. We define equivalent notions of bisimilarity for both variants and characterise them via a bisimulation game. We explain how conditional transition systems are related to featured transition systems for the modelling of software product lines. Furthermore, we show how to compute bisimilarity symbolically via BDDs by defining an operation on BDDs that approximates an element of a Boolean algebra into a lattice. We have implemented our procedure and provide runtime results. This is an extended version of the TASE 2017 paper [1], including all proofs, additional examples, an extension of the formalism to account for deactivation of updates and detailed runtime results

Optimizing investments in cyber hygiene for protecting healthcare users

Cyber hygiene measures are often recommended for strengthening an organization’s security posture, especially for protecting against social engineering attacks that target the human element. However, the related recommendations are typically the same for all organizations and their employees, regardless of the nature and the level of risk for different groups of users. Building upon an existing cybersecurity investment model, this paper presents a tool for optimal selection of cyber hygiene safeguards, which we refer as the Optimal Safeguards Tool (OST). The model combines game theory and combinatorial optimization (0-1 Knapsack) taking into account the probability of each user group to being attacked, the value of assets accessible by each group, and the efficacy of each control for a particular group. The model considers indirect cost as the time employees could require for learning and trainning against an implemented control. Utilizing a game-theoretic framework to support the Knapsack optimization problem permits us to optimally select safeguards’ application levels minimizing the aggregated expected damage within a security investment budget. We evaluate OST in a healthcare domain use case. In particular, on the Critical Internet Security (CIS) Control group 17 for implementing security awareness and training programs for employees belonging to the ICT, clinical and administration personnel of a hospital. We compare the strategies implemented by OST against alternative common-sense defending approaches for three different types of attackers: Nash, Weighted and Opportunistic. Our results show that Nash defending strategies are consistently better than the competing strategies for all attacker types with a minor exception where the Nash defending strategy, for a specific game, performs at least as good as other common-sense approaches. Finally, we illustrate the alternative investment strategies on different Nash equilibria (called plans) and discuss the optimal choice using the framework of 0-1 Knapsack optimization

Family-Based Model Checking with mCRL2

\u3cp\u3eFamily-based model checking targets the simultaneous verfication of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines (SPLs). We present an approach for family-based verification based on the feature μ-calculus μL\u3csub\u3ef\u3c/sub\u3e, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants (a family of products). Via an embedding into the modal μ-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for μLf becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.\u3c/p\u3