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Practical model-based testing : a tools approach
This book gives a practical introduction to model-based testing, showing how to write models for testing purposes and how to use model-based testing tools to generate test suites. It is aimed at testers and software developers who wish to use model-based testing, rather than at tool-developers or academics.The book focuses on the mainstream practice of functional black-box testing and covers different styles of models, especially transition-based models (UML state machines) and pre/post models (UML/OCL specifications and B notation). The steps of applying model-based testing are demonstrated on examples and case studies from a variety of software domains, including embedded software and information systems. From this book you will learn:* The basic principles and terminology of model-based testing* How model-based testing differs from other testing processes* How model-based testing fits into typical software lifecycles such as agile methods and the Unified Process* The benefits and limitations of model-based testing, its cost effectiveness and how it can reduce time-to-market* A step-by-step process for applying model-based testing* How to write good models for model-based testing* How to use a variety of test selection criteria to control the tests that are generated from your models* How model-based testing can connect to existing automated test execution platforms such as Mercury Test Director, Java JUnit, and proprietary test execution environments * Presents the basic principles and terminology of model-based testing* Shows how model-based testing fits into the software lifecycle, its cost-effectiveness, and how it can reduce time to market* Offers guidance on how to use different kinds of modeling techniques, useful test generation strategies, how to apply model-based testing techniques to real applications using case studies
eBook
Complete guide to test automation : techniques, practices, and patterns for building and maintaining effective software projects
Rely on this robust and thorough guide to build and maintain successful test automation. As the software industry shifts from traditional waterfall paradigms into more agile ones, test automation becomes a highly important tools that allows your development teams to deliver software at an ever-increasing pace without compromising quality. Even though it may seem trivial to automate the repetitive tester's work, using test automation efficiently and properly is not trivial. Many test automation endeavors end up in the \"graveyard\" of software projects. There are many things that affect the value of test automation, and also its costs. This book aims to cover all of these aspects in great detail so you can make decisions to create the best test automation solutuion that will not only help your test automation project to succeed, but also allow the entire software project to thrive. One of the most important details that affects the success of the test automation is how easy it is to maintain the automated tests. \"Complete guide to test automation\" provides a detailed hands-on guide to writing highly maintainable test code. What you'll learn: Know the real value to be expected from test automation ; Discover the key traits that will make your test automation project succeed ; Be aware of the different considerations to take into account when planning automated tests vs. manual tests ; Determine who should implement the tests and the implications of this decision ; Architect the test project and fit it to the architecture of the tested application ; Design and implement highly reliable automated tests ; Begin gaining value from test automation earlier ; Integrate test automation into the business processes of the development team ; Leverage test automation to improve your organization's performance and quality, even without formal authority ; Understand how different types of automated tests will fit into your testing strategy, including unit testing, load and performance testing, visual testing, and more.
Book
Bugs in machine learning-based systems: a faultload benchmark
The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and integrating them into the ML-based system safely. Although most of these tools use bugs’ lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses. In this study, we firstly investigate the reproducibility and verifiability of the bugs in ML-based systems and show the most important factors in each one. Then, we explore the challenges of generating a benchmark of bugs in ML-based software systems and provide a bug benchmark namely defect4ML that satisfies all criteria of standard benchmark, i.e. relevance, reproducibility, fairness, verifiability, and usability. This faultload benchmark contains 100 bugs reported by ML developers in GitHub and Stack Overflow, using two of the most popular ML frameworks: TensorFlow and Keras. defect4ML also addresses important challenges in Software Reliability Engineering of ML-based software systems, like: 1) fast changes in frameworks, by providing various bugs for different versions of frameworks, 2) code portability, by delivering similar bugs in different ML frameworks, 3) bug reproducibility, by providing fully reproducible bugs with complete information about required dependencies and data, and 4) lack of detailed information on bugs, by presenting links to the bugs’ origins. defect4ML can be of interest to ML-based systems practitioners and researchers to assess their testing tools and techniques.
Journal Article
The skills that employers look for in software testers
Software testing is an integral part of software development that provides better-quality products and user experiences and helps build the reputation of software companies. Though software testers perform a role that requires specific tasks and skills, in-depth studies of software testers lag behind research studies of other roles within software development teams. In this paper, we aim to create a profile of testers by presenting an empirical analysis of the skills the industry currently needs. We analysed data from 400 job adverts in 33 countries. We mapped the skills on a taxonomy comprising test-related, technical, and domain-specific skills. In addition, we looked at the demand for educational attainment, relevant certifications, and previous experience requirements. Our findings show that employers are mostly interested in skills related to test planning and design, test automation, functional testing, performance testing, and progress reporting. One third of the job advertisers were interested in people with the skills to operate test execution tools. Selenium was the testing tool most in demand. The testers must have strong technical abilities, including programming skills in Java, C#, and SQL. Also, they must handle project management tasks such as estimation, risk management, and quality assurance. Employers do not emphasise domain-specific knowledge, which indicates that they consider testing skills portable across industries. One in seven job adverts asks for a software testing certification. Our study helps clarify the complexity of the testing job and outlines the capabilities one needs to fulfil a software tester’s responsibilities.
Journal Article
Search-based fairness testing for regression-based machine learning systems
ContextMachine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems.Method:We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software.Results:We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches.Conclusion:These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological rule; to measure the fairness of the regression-based ML systems in the context of emergency department wait-time prediction use fairness degree and search-based techniques to approximate it.
Journal Article
Python for Offensive PenTest
Python is an easy-to-learn and cross-platform programming language which has unlimited third-party libraries. Plenty of open source hacking tools are written in Python and can be easily integrated within your script. This book is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to.
eBook