Discrete time software reliability modeling with periodic debugging schedule

Automated testing techniques for software design specification include model-based approaches that generate test cases from a coding or state machine model of the interface, capture-replay methods that require the user to specify and demonstrate each test case individually, and modelling-language approaches that provide templates for abstract test cases. In this paper elucidates a method for automatic software analysis testing for IoT service. Nowadays real-time IoT software is used to satisfy the needs and functions of a user and it is assisted in this by the hardware. Software testing is generally used to detect bugs and errors and eliminate them to produce a high quality, reliable software. The automated testing depends on the kind of approach taken. This testing may be poorly implemented which results in a low quality software or it may be effectively implemented which results in a high quality software. Automated IoT software testing can decrease time and cost. When these techniques are identified, the cost and time can be reduced. The automated testing is based on three steps such as analyzing the test case, test execution and log analyzer. These steps provide user friendly software with high standard. The automated process provides test reusability and repeatability and saves the test coverage cost and time. In this paper compare the automated software testing for IoT transactions and system peripherals such as sensor, DMA controller, internal counters, physical layer, and virtual layer using IoT specification based software testing.

Optimal and adaptive testing for software reliability assessment

This work is motivated by a particular software reliability problem in a unit of flight control software developed by the Indian Space Research Organization (ISRO), in which the testing of the software is carried out in multiple batches, each consisting of several runs. As the errors are found during the runs within a batch, they are noted, but not debugged immediately; they are debugged only at the end of that particular batch of runs. In this work, we introduce a discrete time model suitable for this type of periodic debugging schedule and describe maximum likelihood estimation for the model parameters. This model is used to estimate the reliability of the software. We also develop a method to determine the additional number of error‐free test runs required for the estimated reliability to achieve a specific target with some high probability. We analyze the test data on the flight control software of ISRO. Copyright © 2011 John Wiley & Sons, Ltd.

Software testing is essential in the software development process. Modified Condition / Decision Coverage (MC/DC) is a test case derivation technique that enhances the stability and reliability of software by effectively verifying complex conditions and decision structures. We propose the MCC Test Cases (MTC) generator in this study. This generator generates Multiple Condition Coverage (MCC) test cases using binary numbers to confirm the maximum coverage value of MC/DC testing. The proposed MTC generator utilizes some conditions from the Traffic Alert and Collision Avoidance System (TCAS)-II specification. It converts them into a Comma-Separated Values (CSV) file and then validates the coverage results through the VectorCAST program. So, MC/DC testing was performed using the MCC test case to confirm the maximum coverage value when performing MC/DC tests for each condition of the TCAS-II specification. This research is helpful for the verification of MC/DC test cases by confirming the maximum coverage value when performing MC/DC tests. Moreover, having more test cases increases the likelihood of discovering defects. Therefore, it can improve the efficiency of software test coverage verification, as well as the quality and stability of software.

Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage.

With the increasing complexity of flight control software,it becomes very challenging to improve the reliability of flight control software. There is a kind of software aging bugs which can reduce the system memory and eventually trigger failure, which will seriously affect the reliability of flight control software. Aiming at the bugs, a context-based software aging fault location method is proposed. Thie basic idea of the proposed method is to transfer the main circulation model of the flight control software into a task tree, and then extract main cycle executions runs into a sequence of task tree. By utilizing Cox-Stuart Test, the memory attribute of the central node of the task tree is detected to find the suspicious task. Thereafter, according to the relationship among the task tree series, suspicious tasks are selected to locate defects. The function of suspicious task is the location of the defect. Finally, an experiment is implemented and the results show that the proposed method can not only locate the defect, but also show the call context.

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

A history-based cost-cognizant test case prioritization technique in regression testing

The role of flight software in the spacecraft is becoming more essential if missions are to be considered successful. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1 2</sup> To realize more reliable and continuous independent verification and validation (IV&V) and improve the dependability of flight software, a systems engineering process called Model-based IV&V (MBIVV) was developed and has been applied to flight software for several years. This paper examines the experience of performing MBIVV, a process which augments existing IV&V methods with various potential benefits to improve the dependability of flight software. MBIVV techniques are effective for detecting basic or complex errors and many warnings, all of which may not be identified in existing IV&V processes. Moreover, the techniques can be applied to all or only the critical portions of the target software. The number, level of abstraction, and scope of the models are adjusted to meet the objectives of the IV&V attribute and the complexity of the target flight software. This paper introduces the MBIVV paradigm, activities, and practical applications to demonstrate that this MBIVV is an effective means of ensuring the dependability of flight software.

The Mars 2020 (M2020) Perseverance Rover is NASA&#x0027;s most advanced planetary rover mission to date. It includes a novel Sample Caching Subsystem (SCS) which will collect rock cores for possible future return to Earth, as well as an improved mobility system with enhanced autonomous navigation which will enable it to traverse faster and farther than prior rovers. The development of both systems required extensive flight software and flight hardware testing. To support this testing, we developed the Surface System Development Environment (SSDEV) and used it for a wide variety of testing. SSDEV is a bundled subset of M2020 Flight Software which runs on commercially available Linux computers and can be combined with multiple backend options for simulation and hardware control. The SSDEV architecture enabled our teams to perform much more testing of flight software and flight hardware than would have otherwise been possible. As a secondary benefit, the SSDEV-based test campaigns also helped our teams enter the operations phase of the mission with greater readiness of operations products and tools. In this paper, we summarize the motivation for SSDEV, provide an overview of the SSDEV architecture, list several examples of how SSDEV was used, and summarize lessons learned. SSDEV is not a substitute for integrated testing with flight-like avionics, but it enabled substantially more testing than would have otherwise been possible and also provided some unique benefits. We recommend architectures like SSDEV to future projects that need to perform extensive hardware and software testing using a limited set of flight-like avionics.

Flight control software is the nerve center of flight control system, which has high functional complexity and difficult test. In this paper, a test method based on input field model is proposed. The input space of flight control software is identified, and input domain modeling is carried out. According to the pairwise combination test coverage strategy, a heuristic test case generation algorithm with constraints is used to generate test cases so as to guide the test execution. The example shows that the characteristic modeling of flight control software input domain and the test based on the model can meet the requirements of the sufficiency and coverage of flight control software test, greatly improve the effectiveness of software testing and reduce the test cost.)

Software reliability testing refers to various software testing activities that are driven to achieve a quantitative reliability goal given a priori or lead to a quantitative reliability assessment for the software under test. In this paper we develop a modeling framework for the software reliability testing process, comprising a simplifying model and a generalized model. In both models the software testing action selection process and the defect removal mechanism are explicitly described. Both the discrete-time domain and the continuous-time domain are involved. The generalized model is more accurate or realistic than the simplifying model since the former avoids the assumption that defects are equally detectable and the assumption that defects are removed upon being detected. However simulation examples show that the simplifying model really captures some of essential features of the software testing process after a short initial testing stage. The modeling framework is practically realistic, mathematically rigorous, and quantitatively precise. It demonstrates that the relationship between software testing and delivered software reliability, which was poor understood, can well be formulated and quantified. Rigorous examinations show that several common assumptions adopted in software reliability modeling, including the independence assumption, the exponentiality assumption, and the NHPP assumption, are theoretically false in general. This paper sets a good starting point to further formalize and quantify the software testing process and its relation to delivered software reliability.

Software testing (debugging) in order to reduce costs in terms of changes in the software reliability and testing costs, need to know in advance is more efficient. In this paper, proposes the reliability model with monotonic intensity functions (Power-Law, Musa-Okumoto and Comperz model), which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimation and bisection method, model selection based on mean square error and coefficient of determination, for the sake of efficient model, were employed. Analysis of failure using real data set for the sake of proposing monotonic intensity function was employed. This analysis of failure data compared with monotonic intensity function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the proposed monotonic intensity function is more efficient in terms of reliability in this area. Thus, monotonic intensity function can also be used as an alternative model. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help. Keywords: Laplace Trend Test, Monotonic Intensity Function, NHPP, Software Reliability

Most real-time embedded control software feature complex interactions with asynchronous inputs and environment objects, and a meaningful simulation of a real-time control software specification requires realistic simulation of its environment. Two problems that need to be addressed in the simulation of a target software system and its environment: First, integration and simulation of the specifications of a target software system and its artificial environment are often performed too late in the lifecycle to provide any significant value. Second, real world objects in the environment usually have spatial characteristics ( form ) such as shape, motion, etc. that must be specified for simulation, and there is no method to express these spatial characteristics at various levels of abstraction that are adequate for the required simulation fidelity.To address these problems, we have developed a method that supports incremental specification and simulation of both the target software system and its environmental objects. The method includes: (1) a specification method for behavior, function, and form integrated objects; (2) form specification primitives that abstract common spatial characteristics of real world objects, their typical spatial relations, and spatial interactions; and (3) a methodology that refines, verifies, and validates behavior, function, and form specification of both the real-time embedded control software and its environment in a systematic and incremental manner. The proposed specification, verification, and validation method has been applied to a robot control system example to demonstrate its effectiveness and usefulness.

International standards for software testing have been published in 2013 and are now widely used in industry and academia. However, these standards are difficult to use for the analysis of testing for complex software. Complex software has a huge number of components and an enormous number of relations between them. If we analyze such complex software according to the software testing standards, the results of the analysis will have a huge number of testing patterns or cases. Therefore, planning a project of complex software testing is impossible, and even if we could plan it, the plan would be infeasible. We propose a method of testing analysis for complex software by using definitions of software architecture in international standards. We believe that the outcomes of the proposed method are software-test-architecture. The software-test-architecture helps to analyze complex software, plan software testing, and develop test cases. We select existing case studies that have testing on a large and complex software, and discuss techniques to apply the proposed method for the case studies.