Aspects Of Software Testing Research Articles

RESEARCH ON ARTIFICIAL INTELLIGENCE TOOLS FOR AUTOMATING THE SOFTWARE TESTING PROCESS

The subject matter of the article is artificial intelligence (AI) tools for automating the software testing process. The rapid development of the software development industry in recent decades has led to a significant increase in competition in the IT technology market and, as a result, stricter requirements for corresponding products and services. AI-driven test automation is becoming increasingly relevant due to its ability to solve complex tasks that previously required significant human resources. The goal of the work is to investigate the possibilities of using AI technologies to automate manual testing processes, which will increase testing efficiency, reduce costs, and improve software quality. The following tasks were solved in the article: analysis of existing tools and approaches to test automation using AI; development of a conceptual model of a system that integrates AI into the testing process; exploring the potential of AI to automate various aspects of software testing, such as generating test scenarios, detecting defects, predicting errors, and automatically analyzing test results. The following methods are used: theoretical analysis of the literature and existing solutions in the field of test automation, experimental study of the effectiveness of the proposed test automation methods. The following results were obtained: the concept of a system that integrates AI technologies for automating software testing is presented. It has been found that the use of AI allows automating routine testing tasks, significantly reducing the number of human errors, and improving the quality of software products and the effectiveness of verification and validation processes. Conclusions: The development and implementation of AI-based testing automation systems are extremely relevant and promising. The use of AI technologies makes it possible to significantly increase the efficiency of testing, reduce the costs of its implementation, and improve the quality of software. The proposed approach to the development of an AI-based test automation system can be used as a basis for further research and development in this field.

International Comparative Studies on the Software Testing Profession

This work attempts to fill a gap by exploring the human dimension in particular, by trying to understand the motivation of software professionals for taking up and sustaining their careers as software testers. Towards that goal, four surveys were conducted in four countries - India, Canada, Cuba, and China - to try to understand how professional software engineers perceive and value work-related factors that could influence their motivation to start or move into software testing careers. From our sample of 220 software professionals, we observed that very few were keen to take up testing careers. Some aspects of software testing, such as the potential for learning opportunities and the importance of the job, appear to be common motivators across the four countries, whereas the treatment of testers as second-class citizens and the complexity of the job appeared to be the most prominent de-motivators.

An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning

Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models.

Testing Relative to Usage Scope

Coverage criteria provide a useful and widely used means to guide software testing; however, indiscriminately pursuing full coverage may not always be convenient or meaningful, as not all entities are of interest in any usage context. We aim at introducing a more meaningful notion of coverage that takes into account how the software is going to be used. Entities that are not going to be exercised by the user should not contribute to the coverage ratio. We revisit the definition of coverage measures, introducing a notion of relative coverage. According to this notion, we provide a definition and a theoretical framework of relative coverage, within which we discuss implications on testing theory and practice. Through the evaluation of three different instances of relative coverage, we could observe that relative coverage measures provide a more effective strategy than traditional ones: we could reach higher coverage measures, and test cases selected by relative coverage could achieve higher reliability. We hint at several other useful implications of relative coverage notion on different aspects of software testing.

Cloud-Based Test Tools: A Brief Comparative View

Abstract The concept of virtualization has brought life to the new methods of software testing. With the help of cloud technology, testing has become much more popular because of the opportunities it provides. Cloud technologies provides everything as a service, hence the software testing is also provided as a service on cloud with the privileges of lower cost of testing, and relatively less effort. There are various cloud-based test tools focusing on different aspects of software testing such as load tests, regression tests, stress tests, performance tests, scalability tests, security tests, functional tests, browser performance tests, and latency tests. This paper investigates the cloud-based testing tools focusing on different aspects of software testing.

State Transition Testing Approach for Ad hoc Networks using Ant Colony Optimization

Nowadays, telecommunication software organizations are challenged to provide high-quality software to customers within their estimated time and budget in order to stay competitive within the market. Because quality is a defining aspect of the product, it is essential for a project manager to stay alert throughout the project lifecycle. Quality has a direct bearing on customer satisfaction, and if a company produces high-quality products, satisfied customers will rank it highly in customer satisfaction surveys. Additionally, dissatisfied customers are more vocal in their criticisms. Therefore, testing is an important step to produce more reliable systems. In this paper we address two important aspects of software testing for ad hoc network protocols. The first one is by integrating a high-level testing approach based on state transition on top of a network simulator in order to fill a perceived gap in existing network simulators. The second one is reducing testing effort by eliminating redundant test cases, in order to effectively improve the result accuracy of existing network simulators. In this paper, we implemented an automated state transition testing approach for wireless network routing protocols, using an improved Ant Colony Optimization (ACO) algorithm. The expected result is to provide maximum coverage in terms of states and transitions.

Improving the Technical Aspects of Software Testing in Enterprises

Many software developments projects fail due to quali- ty problems. Software testing enables the creation of high quality software products. Since it is a cumbersome and expensive task, and often hard to manage, both its technical background and its organizational implementation have to be well founded. We worked with regional companies that develop software in order to learn about their distinct weaknesses and strengths with re- gard to testing. Analyzing and comparing the strengths, we de- rived best practices. In this paper we explain the project's back- ground and sketch the design science research methodology used. We then introduce a graphical categorization framework that helps companies in judging the applicability of recommendations. Eventually, we present details on five recommendations for tech- nical aspects of testing. For each recommendation we give im- plementation advice based on the categorization framework.

Insights into software testing

AbstractMARTIN WOODWARD aims to provide a brief insight into some of the many aspects of software testing. The various stages required when testing large software systems are outlined and then a selection of possible testing techniques are described and illustrated. Given the increased use of formal specifications in software development, one could exploit such a specification during testing and some of the ways this might be done are indicated. Similarly, the nature of the design approach used in the development of the software or even the nature of the particular application being developed can lead to particular test strategies. Finally, testing is not purely a technical matter; people issues and management of the process have a significant impact on the success or otherwise of this activity. A brief discussion of these topics rounds off this review. Copyright © 2001 John Wiley & Sons, Ltd.

Tools and techniques for the testing of safety-critical software

As a result of the DTI/SERC research initiative on safety-critical systems a major study has been conducted into the benefits and shortcomings of the available tools and techniques for computer-aided testing of high-integrity software. The work described forms part of the CONTESSE project, which is concerned with various aspects of software testing. Working from experience and knowledge accumulated by a number of leading UK companies it has been possible to assemble data that should prove valuable to all organisations engaged in the development or licensing of safety-critical computer-based systems. Both strengths and weaknesses of current methods are discussed. The article is an integral part of the DTI/SERC initiative to disseminate such knowledge to a wider audience.

Comparing the Effectiveness of Software Testing Strategies

This study applies an experimentation methodology to compare three state-of-the-practice software testing techniques: a) code reading by stepwise abstraction, b) functional testing using equivalence partitioning and boundary value analysis, and c) structural testing using 100 percent statement coverage criteria. The study compares the strategies in three aspects of software testing: fault detection effectiveness, fault detection cost, and classes of faults detected. Thirty-two professional programmers and 42 advanced students applied the three techniques to four unit-sized programs in a fractional factorial experimental design. The major results of this study are the following. 1) With the professional programmers, code reading detected more software faults and had a higher fault detection rate than did functional or structural testing, while functional testing detected more faults than did structural testing, but functional and structural testing were not different in fault detection rate. 2) In one advanced student subject group, code reading and functional testing were not different in faults found, but were both superior to structural testing, while in the other advanced student subject group there was no difference among the techniques. 3) With the advanced student subjects, the three techniques were not different in fault detection rate. 4) Number of faults observed, fault detection rate, and total effort in detection depended on the type of software tested. 5) Code reading detected more interface faults than did the other methods. 6) Functional testing detected more control faults than did the other methods.