Optimal strategies for testing nondeterministic systems

Reactive systems are characterized by the interaction with the environment, where the exchange of the input and output stimuli, usually, occurs asynchronously. Systems of this nature, in general, require a rigorous testing activity over their developing process. Therefore model-based testing has been successfully applied over asynchronous reactive systems using Input Output Labeled Transition Systems (IOLTSs) as the basis. In this work we present a reactive testing tool to check conformance, generate test suites and run test cases using IOLTS models. Our tool can check whether the behavior of an implementation under test (IUT) complies with the behavior of its respective specification. We have implemented a classical conformance relation ioco and a more general notion of conformance based on regular languages. Further, the tool provides a test suite generation in a black-box testing setting for finding faults over IUTs according to a specific domain. We have also described some case studies to probe the tool's functionalities and also to highlight a comparative analysis on both conformance approaches. Finally, we offer experiments to evaluate the performance of our tool using several scenarios.

We describe a model-based construction of an online tester for black-box testing of the implementation under test(IUT). The external behaviour of the IUT is modelled as an output observable nondeterministic EFSM with the assumption that all transition paths are feasible. A test purpose is attributed to the IUT model by a set of Boolean variables called traps that are used to measure the progress of the test run. These variables are associated with the transitions of the IUT model. The situation where all traps have been reached means that the test purpose has been achieved. We present a way to construct a tester that at runtime selects a suboptimal test path from trap to trap by finding the shortest path to the next unvisited trap. The principles of reactive planning are implemented in the form of the decision rules of selecting the shortest paths at runtime. The decision rules are constructed in advance from the IUT model and the test purpose. Preliminary experimental results confirm that this method clearly out performs random choice and is better than the anti-ant algorithm in terms of resultant test sequence length to achieve the test purpose

This paper describes a research result on automatic generation of abstract test cases from INAP protocol in formal specifications by using and applying many existing related algorithms and techniques such as the testing framework, rural Chinese postman tour and UTO (Unique Input Output) sequence concepts. We use the I/OFSM (Input/Output Finite State Machine) generated from SDL specifications and a characterizing sequence, called Unique Input/Output (UIO) sequence, is defined for the I/O FSM. The UIO sequence is combined with the concept of rural Chinese postman tour, without reliable reset capability, to obtain an optimal test sequence. To develop this method, first of all, we establish a framework leading to formal testing methodology which can be used to assess the conformance of an IUT (Implementation Under Test) to the behaviours specified in an I/OFSM which is equivalent to corresponding FDT (Formal Description Technique) specification. By this framework, we present a methodology for automatic test generation from FDT specifications. A prototype of the proposed framework has been built with special attention to real application in order to generate the executable test cases in an automatic way.

Software testing is an integral part of software development lifecycle. Lack of testing can often lead to disastrous consequences including lost of data, fortunes, and even lives. Despite its importance, current software testing practice lacks automation, and is still primarily based on highly manual processes from the generation of test cases up to the actual execution of the test. Although the emergence of helpful automated testing tools in the market is blooming, their adoptions are lacking as they do not adequately provide the right level abstraction and automation required by test engineers. JTst is a Java based automated unit testing tool that addresses some of the aforementioned issues. The main novel features are the fact that JTst automates the test preparation activities, facilitates the test data generation through recombination, and allows concurrent execution of test data, in order to encourage higher product quality at lower testing costs.

Quality is a key property of modern software systems. In addition, to quality improvement strategies such as CMM or CMMI and manual checking approaches such as reviews or inspections, software testing is the major quality assurance activity in most projects. Literally speaking, software testing is nearly as essential as writing code itself. Due to its prominent role there are quite a number of commercial and open-source test tools available. However, the question remains if an organisation should rely on a single tool or if it should follow a more general strategy using several tools? Practical experience has shown that the latter strategy is more promising by covering multiple quality aspects that cannot be covered by a single tool, although it requires a means of communication between tools (i.e., a tool independent test language). This paper introduces a XML-based and test tool independent test modeling language that support creating general test cases. The language separates test logic from test tool specific code. Thus, a test case only has to be specified once and can then be executed by several test tools.

Software Testing is known as an important part in software development as the product needs to meet the user's expectations and requirements. The testing tool is developed along with the development of automated software testing to act as a medium to test the software, and the existence of these tools eventually cut down the human's effort, energy, and time. Software testing tools are the key assets of a project that can improve productivity and software quality. As there are many tools in the market nowadays, the process of selecting the tool should be explained in detail and specific towards its functions and efficiency. This paper aims to help the authors in making the tool selection from the various versions of testing tools. This study finally chose the most suitable tool according to the research needs.

Coordination is playing a key role in complex cyber-physicalsystems (CPSs). The complexity and importance of coordination models and languages for CPSs necessarily lead to a higher relevance of testing during development of CPSs. Model-based testing is a promising technology to test the conformance or non-conformance relation between the implementation-under-test (IUT) and its specification. In this paper, we present an approach to test the conformance relation tiococ(Timed Input-Output Conformance) between the implementation of a timed Reo connector and its specification given by a timed constraint automaton (TCA). An algorithm to generate test cases from a TCA is proposed and the testing approach is implemented in UPPAAL.

Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement

Software testing is the key to ensuring software quality. Test case generation and software fault localization are two important research objects of testing. Spectrum-based fault localization (SBFL) method is a mainstream dynamic fault localization method, which inputs test cases into the program for execution and collects statement coverage information and execution results. By analysing this information, a fault report can be generated. The performance of SBFL is affected by test case quality. Therefore, this paper proposes a test case generation method based on improved genetic algorithm to assist SBFL. This method uses a small number of initial test cases to execute the program, then uses the XGBoost model to pre-process the spectrum information and generates a list of potential fault statements to guide the iterative process of the genetic algorithm. The experimental results show that the test cases generated using the improved genetic algorithm have certain advantages over the test cases generated by general genetic algorithm and the baseline test cases.

One of the main concerns in the software industry continues to be the development of high quality software. This concern will be exacerbated as software systems become more complex. The training of software developers continues to grow in academia since more institutions are offering software engineering (SE) courses. However, the list of topics that are expected to be covered in this course leaves little or no time for topics that focus on developing quality software, such as software testing and the use of testing tools.In this paper we describe an approach that non-intrusively integrates the use of software testing tools in SE courses. The cornerstone of our approach is the interaction students have with a Web-Based Repository of Software Testing Tools (WReSTT) that contains tutorials on testing concepts and testing tools. WReSTT employs both collaborative learning and social networking features that are attractive to students. We present the results of preliminary study performed in two SE courses that show how using the resources in WReSTT can potentially impact the students' understanding of software testing and the use of testing tools.

It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze many lines of code and detect vulnerabilities and possible attack vectors by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in testing tools. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found constrained data generation applications were systematized, and the advantages and limitations of approaches specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to improve automated testing tools with data generated by adversarial attacks.

Software testing plays a key role in Software Development. There are two approaches to software testing i.e. Manual Testing and Automated Testing which are used to detect the faults. There are numbers of automated software testing tools with different purposes, but it is always a problem to select a software testing tool according to the needs. Web service is broadly utilised idea these days due to quick promotion of Web services and less literature is accessible with respect to web administration's performance. Web applications are hard to test in contrast with customary applications particularly as far as Performance testing, for example, unpredictable load, response time and so on. In this investigation correlation of performances testing tools i.e. Apache JMeter, NeoLoad, and Loadster is done based on various parameters. Performance parameters results generated by these performance testing tools have been evaluated and assessed in terms of usability test parameters & performance parameters.

This paper presents work on generation of specification-driven test cases based on quasirandom (low-discrepancy) sequences instead of pseudorandom numbers. This approach is novel in software testing. This enhanced uniformity of quasirandom sequences leads to faster generation of test cases covering all possibilities. We demonstrate by examples that quasirandom sequences can be a viable alternative to pseudorandom numbers in generating test cases. In this paper, we present a method that can generate test cases from a decision table specification more effectively via quasirandom numbers. Analysis of a simple problem in this paper shows that quasirandom sequences achieve better data than pseudorandom numbers, and have the potential to converge faster and so reduce the computational burden. The use of different quasirandom sequences for generating test cases is presented in this paper.

As we know, software testing is an important part of software development lifecycle. More than 50% of the whole system development work and total cost were spend on the software testing. And it’s estimated that almost 60% of the total test time and cost were spent on the design of test cases. In recent years, the automated testing gradually replaces the traditional manual testing to become an important branch of software testing. In automated testing, the most important thing is to design and generate valid test case automatically. In this paper, In order to reduce the workload of testers on the test case generation, improve the test efficiency and makes test experiences be passed on, we design a common description method for the test case based on ontology; propose a semantic similarity measure method to retrieve the usable test cases from test case library based on WordNet; establish the relationship between the test case and test requirement though rules to modify the retrieved test case; generate the final test case sequence. At last, the practical applicability of the approach is evaluated through an experiment. Keywords-software testing; ontology; test case generation; semantic similarity measure

Symbolic execution is widely used in software test cases automatic generating, but itpsilas hard to use it in embedded software testing, for embedded software is related to hardware too close. And at present, embedded software testing tools can only generate flow chart and test cases' coverage information, can not generate test cases that satisfy designated coverage automatically. This paper brings forward an idea that realizes embedded software test cases automatic generating using symbolic execution. First translate the embedded program into an intermediate language program, which preserve all the necessary information from the origin embedded program and according hardware, then symbolic execute the intermediate language program to generate test cases automatically.