Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Effective testing for concurrency bugs

In the current multi-core era, concurrency bugs are a serious threat to software reliability. As hardware becomes more parallel, concurrent programming will become increasingly pervasive. However, correct concurrent programming is known to be extremely challenging for developers and can easily lead to the introduction of concurrency bugs. This dissertation addresses this challenge by proposing novel techniques to help developers expose and detect concurrency bugs. We conducted a bug study to better understand the external and internal effects of real-world concurrency bugs. Our study revealed that a significant fraction of concurrency bugs qualify as semantic or latent bugs, which are two particularly challenging classes of concurrency bugs. Based on the insights from the study, we propose a concurrency bug detector, PIKE that analyzes the behavior of program executions to infer whether concurrency bugs have been triggered during a concurrent execution. In addition, we present the design of a testing tool, SKI, that allows developers to test operating system kernels for concurrency bugs in a practical manner. SKI bridges the gap between user-mode testing and kernel-mode testing by enabling the systematic exploration of the kernel thread interleaving space. Our evaluation shows that both PIKE and SKI are effective at finding concurrency bugs.Im gegenwärtigen Multicore-Zeitalter sind Fehler aufgrund von Nebenläufigkeit eine ernsthafte Bedrohung der Zuverlässigkeit von Software. Mit der wachsenden Parallelisierung von Hardware wird nebenläufiges Programmieren nach und nach allgegenwärtig. Diese Art von Programmieren ist jedoch als äußerst schwierig bekannt und kann leicht zu Programmierfehlern führen. Die vorliegende Dissertation nimmt sich dieser Herausforderung an indem sie neuartige Techniken vorschlägt, die Entwicklern beim Aufdecken von Nebenläufigkeitsfehlern helfen. Wir führen eine Studie von Fehlern durch, um die externen und internen Effekte von in der Praxis vorkommenden Nebenläufigkeitsfehlern besser zu verstehen. Diese ergibt, dass ein bedeutender Anteil von solchen Fehlern als semantisch bzw. latent zu charakterisieren ist -- zwei besonders herausfordernde Klassen von Nebenläufigkeitsfehlern. Basierend auf den Erkenntnissen der Studie entwickeln wir einen Detektor (PIKE), der Programmausführungen daraufhin analysiert, ob Nebenläufigkeitsfehler aufgetreten sind. Weiterhin präsentieren wir das Design eines Testtools (SKI), das es Entwicklern ermöglicht, Betriebssystemkerne praktikabel auf Nebenläufigkeitsfehler zu überprüfen. SKI füllt die Lücke zwischen Testen im Benutzermodus und Testen im Kernelmodus, indem es die systematische Erkundung der Kernel-Thread-Verschachtelungen erlaubt. Unsere Auswertung zeigt, dass sowohl PIKE als auch SKI effektiv Nebenläufigkeitsfehler finden

Compilation Optimizations to Enhance Resilience of Big Data Programs and Quantum Processors

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), exist, though they can be difficult to implement, expensive, and may be limited to only protecting against errors in specific locations. Researchers have been exploring software detection and correction methods as an alternative, commonly trading either overhead in execution time or memory usage to protect against faults. Quantum computers, a relatively recent advancement in computing technology, experience similar errors on a much more severe scale. The errors are more frequent, costly, and difficult to detect and correct. Error correction algorithms like Shor’s code promise to completely remove errors, but they cannot be implemented on current noisy intermediate-scale quantum (NISQ) systems due to the low number of available qubits. Until the physical systems become large enough to support error correction, researchers instead have been studying other methods to reduce and compensate for errors. In this work, we present two methods for improving the resilience of classical processes, both single- and multi-threaded. We then introduce quantum computing and compare the nature of errors and correction methods to previous classical methods. We further discuss two designs for improving compilation of quantum circuits. One method, focused on quantum neural networks (QNNs), takes advantage of partial compilation to avoid recompiling the entire circuit each time. The other method is a new approach to compiling quantum circuits using graph neural networks (GNNs) to improve the resilience of quantum circuits and increase fidelity. By using GNNs with reinforcement learning, we can train a compiler to provide improved qubit allocation that improves the success rate of quantum circuits

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

An empirical study on mutation testing of WS-BPEL programs

Nowadays, applications are increasingly deployed as Web services in the globally distributed cloud computing environment. Multiple services are normally composed to fulfill complex functionalities. Business Process Execution Language for Web Services (WS-BPEL) is an XML-based service composition language that is used to define a complex business process by orchestrating multiple services. Compared with traditional applications, WS-BPEL programs pose many new challenges to the quality assurance, especially testing, of service compositions. A number of techniques have been proposed for testing WS-BPEL programs, but only a few studies have been conducted to systematically evaluate the effectiveness of these techniques. Mutation testing has been widely acknowledged as not only a testing method in its own right but also a popular technique for measuring the fault-detection effectiveness of other testing methods. Several previous studies have proposed a family of mutation operators for generating mutants by seeding various faults into WS-BPEL programs. In this study, we conduct a series of empirical studies to evaluate the applicability and effectiveness of various mutation operators for WS-BPEL programs. The experimental results provide insightful and comprehensive guidance for mutation testing of WS-BPEL programs in practice. In particular, our work is the systematic study in the selection of effective mutation operators specifically for WS-BPEL programs

Data Management in Microservices: State of the Practice, Challenges, and Research Directions

We are recently witnessing an increased adoption of microservice architectures by the industry for achieving scalability by functional decomposition, fault-tolerance by deployment of small and independent services, and polyglot persistence by the adoption of different database technologies specific to the needs of each service. Despite the accelerating industrial adoption and the extensive research on microservices, there is a lack of thorough investigation on the state of the practice and the major challenges faced by practitioners with regard to data management. To bridge this gap, this paper presents a detailed investigation of data management in microservices. Our exploratory study is based on the following methodology: we conducted a systematic literature review of articles reporting the adoption of microservices in industry, where more than 300 articles were filtered down to 11 representative studies; we analyzed a set of 9 popular open-source microservice-based applications, selected out of more than 20 open-source projects; furthermore, to strengthen our evidence, we conducted an online survey that we then used to cross-validate the findings of the previous steps with the perceptions and experiences of over 120 practitioners and researchers. Through this process, we were able to categorize the state of practice and reveal several principled challenges that cannot be solved by software engineering practices, but rather need system-level support to alleviate the burden of practitioners. Based on the observations we also identified a series of research directions to achieve this goal. Fundamentally, novel database systems and data management tools that support isolation for microservices, which include fault isolation, performance isolation, data ownership, and independent schema evolution across microservices must be built to address the needs of this growing architectural style