Reducing Field Failures in System Configurable Software: Cost-Based Prioritization

Background: In Regression testing when any changes made to already tested program it should not affect to other part of program. When some part of code is modified then it is necessary to validate the modified code. In the life cycle of software development, regression testing is an important and expensive activity. Objective: Our objective is to save regression testing time by reducing the cost and time of retesting of the modified code as well as affected components of the program. Objective is to prioritize the test case for object oriented program based on early fault detection. Methods: A new model is proposed using fuzzy inference system for regression testing. This paper presents a fuzzy inference system based on the use of inputs like fault detection rate, execution time and requirement coverage. The proposed system allows tester to prioritize test case based on linguistic rules. Results: In this paper performance is measured by using Average Percentage Fault Detection metric. We analysed for both prioritized and non-prioritized test suite using average percentage fault detection metric. Conclusion: We developed fuzzy expert model which helps to takes better decision than other expert system for regression testing. We conclude that, prioritization of test case will decrease the regression testing time.

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

The nursery and finishing phases are critical for profitability and sustainability in swine production, but effective methods for early disease detection in these stages remain underdeveloped. This study used routinely collected production indicators from nursery (42–56 days postfarrowing) and finishing lots (115–120 days postfarrowing) to create production benchmark curves for anomaly detection. These curves were developed for farms without diagnosed health challenges and compared to those with diagnosed health issues, based on tissue submissions and diagnostic codes (Dx codes). Statistical methods such as resampling techniques, Bayesian statistics, and standard deviation (SD) thresholds were employed to build the benchmark curves. The main objective was to test and compare the benchmarks using detection, early detection rate (EDR), time-to-detect (TTD), and false positive rate (FPR). Results showed that bootstrapping (BOOT), jackknife (JK), and Markov chain Monte Carlo (MCMC) methods provided the highest EDRs, although they were prone to false positives. For nursery lots, it was observed that using cumulative average with one SD for feed disappearance (EDR 49.2% and FPR 9.8%) and estimated weight (EDR 47.2% and FPR 8.8%) showed the best balance between EDR and FPR, and using MCMC for mortality showed the best balance between EDR and FPR (EDR 38.8% and FPR 13.8%). For finishing lots, using cumulative average with one SD showed a more balanced performance with FPR below 14.0% and EDR of 21.3% for feed disappearance, 67.4% weight, and 59.7% for mortality. These findings demonstrate the potential of using production indicators for early health challenge detection in swine operations.

Novel adaptive fault detection method based on kernel entropy component analysis integrating moving window of dissimilarity for nonlinear dynamic processes

Researchers have explored the application of combinatorial interaction testing (CIT) methods to construct samples to drive systematic testing of software system configurations. Applying CIT to highly-configurable software systems is complicated by the fact that, in many such systems, there are constraints between specific configuration parameters that render certain combinations invalid. Many CIT algorithms lack a mechanism to avoid these. In recent work, automated constraint solving methods have been combined with search-based CIT construction methods to address the constraint problem with promising results. However, these techniques can incur a non-trivial overhead. In this paper, we build upon our previous work to develop a family of greedy CIT sample generation algorithms that exploit calculations made by modern Boolean satisfiability (SAT) solvers to prune the search space of the CIT problem. We perform a comparative evaluation of the cost-effectiveness of these algorithms on four real-world highly-configurable software systems and on a population of synthetic examples that share the characteristics of those systems. In combination our techniques reduce the cost of CIT in the presence of constraints to 30 percent of the cost of widely-used unconstrained CIT methods without sacrificing the quality of the solutions.

In this paper, a novel robust fault detection (FD) approach called parity space vector machine (PSVM) is proposed for linear discrete-time systems. Aiming to achieve a tradeoff between false alarm rate (FAR) and FD rate (FDR) simultaneously, we focus our study on an integrated design of parity space-based FD in the context of residual generation and residual evaluation. Without a prior knowledge of the distribution of the unknown inputs, we propose to construct a PSVM model and formulate the underlying FD problem as a distribution-free Bayes optimal classifier, where the FAR and FDR indicate the worst-case classification accuracies of future residuals for the fault free case and faulty case. Then a bank of parity space vectors and corresponding thresholds can be designed integratedly by applying the techniques of the minimum error minimax probability machine and, at the same time, an optimal tradeoff between FAR and FDR is achieved. Finally, the effectiveness of the proposed approach is demonstrated on a longitudinal control system of unmanned aerial vehicle and further comparison with a traditional parity space-based FD is also addressed.

Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery

This paper proposes a new scheme for constructing software reliability growth models (SRGM) based on a nonhomogeneous Poisson process (NHPP). The main focus is to provide an efficient parametric decomposition method for software reliability modeling, which considers both testing efforts and fault detection rates (FDR). In general, the software fault detection/removal mechanisms depend on previously detected/removed faults and on how testing efforts are used. From practical field studies, it is likely that we can estimate the testing efforts consumption pattern and predict the trends of FDR. A set of time-variable, testing-effort-based FDR models were developed that have the inherent flexibility of capturing a wide range of possible fault detection trends: increasing, decreasing, and constant. This scheme has a flexible structure and can model a wide spectrum of software development environments, considering various testing efforts. The paper describes the FDR, which can be obtained from historical records of previous releases or other similar software projects, and incorporates the related testing activities into this new modeling approach. The applicability of our model and the related parametric decomposition methods are demonstrated through several real data sets from various software projects. The evaluation results show that the proposed framework to incorporate testing efforts and FDR for SRGM has a fairly accurate prediction capability and it depicts the real-life situation more faithfully. This technique can be applied to wide range of software systems.

User requirements are the building blocks for development of software applications. User requirements decide the width and breadth of any software. Nowadays, test scenarios are prepared from the user requirements, which give the test engineers ample scope to review the test plan thoroughly before doing software testing. Regression testing is carried out to know the effect of requirement changes on the functionalities and performance of the software. Test scenario prioritization, which is one of the techniques to perform regression testing, maximizes the ease of debugging for the system under test. The code based test scenario prioritization and model based test scenario prioritization have their own limitations. So, to achieve ease of debugging and to get more time for reviewing the test plans, researchers are now working on test scenario prioritization using requirements collected from the end users. In this paper, we propose an approach named Requirement based test scenario prioritization to prioritize test scenarios using requirements collected from end users for developing software applications. The user’s functional requirements are collected and is assigned with some weight depending upon different factors like complexity of implementing the requirements, type of release of the requirements, requirement volatility, coupling between requirements etc. Test scenarios are generated from requirements collected from the end users. Then, the final priority weight of each test scenario is found out by considering the weight of each requirement covered by the corresponding test scenario and the percentage of requirements coverage made by each test scenario. The test scenarios are prioritized based on the final priority weight. The proposed approach is evaluated using average percentage of fault detection metric and is found to be very efficient in early test scenario prioritization and detection of faults.

Combinatorial interaction testing (CIT) stands as one of the efficient testing techniques that have been used in different applications recently. The technique is useful when there is a need to take the interaction of input parameters into consideration for testing a system. The key insight the technique is that not every single parameter may contribute to the failure of the system and there could be interactions among these parameters. Hence, there must be combinations of these input parameters based on the interaction strength. This technique has been used in many applications to assess its effectiveness. In this paper, we are addressing the effectiveness of CIT for a real-world case study using model-based mutation testing experiments. The contribution of the paper is threefold: First we introduce an effective testing application for CIT; Second, we address the effectiveness of increasing the interaction strength beyond the pairwise (i.e., interaction of more than two parameters); Third, model-based mutation testing is used to mutate the input model of the program in contrast to the traditional code-based mutation testing process. Experimental results showed that CIT is an effective testing technique for this kind of application. In addition, the results also showed the usefulness of model-based mutation testing to assess CIT applications. For the subject of this case study, the results also indicate that 3-way test suite (i.e., interaction of three parameters) could detect new faults that can not be detected by pairwise.

Cracks in shafts have long been identified as factor limiting the safe and reliable operation of gearbox, they sometimes result in disastrous failure of equipment (tooth,.) and, more often, in costly process, upsets, repairs and replacement of equipment. This paper represents the utilization of dynamic model of a simple stage gearbox (with and without defects) and the Hilbert Transform Demodulation in the early detection and condition monitoring of shaft crack fault. Then, the vibration signal is analyzed using the proposed approach. The used approach is compatible for such diagnosis and gives valuable information about crack effects defect presence. The obtained results of early detection of shaft crack fault using the instantaneous phase are represented in this paper.

Fault detection using bispectral features and one-class classifiers

In this paper, a model‐based fault detection method for fault detection of gearboxes in offshore wind turbines is presented. The main aim of this paper is to support conceptual studies with more emphasis on early fault detection methods for floating wind turbines. The method is introduced and applied on a 5‐MW reference gearbox installed on a spar‐type floating wind turbine. Faults applied on the main bearing, high and intermediate speed shaft bearings, and the planet bearing are examined with this method. The gearbox is modelled in a multibody simulation environment with high fidelity. The 5‐MW gearbox model used in this study is an offshore reference gearbox that consists of 3 stages, 2 planetary and 1 parallel stages, which is supported in a 4‐point configuration layout. The bearing faults are detected through the angular velocity error function. The angular velocity measurements are performed at input, output shafts, and 2 additional sensors inside the gearbox. The aim of the method is also to detect the fault through the external sensors—input and output shafts—measurements, which can be embedded in the existing control system. The results reveal the possibility of early fault detection by this method for large floating wind turbines.

Software testing provides a means to reduce errors, cut maintenance and overall software costs. Numerous software development and testing methodologies, tools, and techniques have emerged over the last few decades promising to enhance software quality. While it can be argued that there has been some improvement it is apparent that many of the techniques and tools are isolated to a specific lifecycle phase or functional area. This paper presents a set of best practice models and techniques integrated in optimized and quantitatively managed software testing process (OptimalSQM), expanding testing throughout the SDLC. Further, we explained how can Quantitative Defect Management Model be enhanced to be practically useful for determining which activities need to be addressed to improve the degree of early and cost-effective software fault detection with assured confidence is proposed. To enable software designers to achieve a higher quality for their design, a better insight into quality predictions for their design choices, test plans improvement using Simulated Defect Removal Cost Savings model is offered in this paper.

Performance analysis of software reliability growth models with testing-effort and change-point