A proposal of postgraduate programme for software testing specialization

: With regard to software testing in DoD, we can summarize our conclusions in two fundamental ideas. First, DoD knows how to produce quality software at low cost. This is because organizations such as DoD STEP, Army STEP, and Software Engineering Institute have already researched and documented policies for DoD. A few commercial software developers practice many of the DoD policies and directives now, and produce quality software (for example, IBM FSC Houston). Second, quality cannot be tested into software. Only a well-defined, well-disciplined process with a continuous improvement cycle can ensure software quality. However, testing cannot be underestimated. Systematic testing activities that detect error earliest in the life cycle are necessary to drive process improvement and optimize the development of quality software. Such testing methods as formal inspection find defects early. This enables cost- effective error resolution, identification and removal of defect causes, and thus, prevention of future defect insertion. If practiced with discipline, such methods can evolve a self-correcting software development process that is stable, modeled, measured, and therefore, predictable. This development process engineers quality software faster at reduced cost.... Software testing, Software quality, Formal inspection, Quality control.

Background: Software testing and software security have become one of the most important parts of an application. Many studies have explored each of these topics but there is a gap wherein the relation of software security and software testing in general has not been explored. Objective: This study aims to conduct a systematic literature review to capture the current state-of-the-art in software testing related to security. Methods: The search strategy obtains relevant papers from IEEE Xplore and ScienceDirect. The results of the search are filtered by applying inclusion and exclusion criteria. Results: The search results identified 50 papers. After applying the inclusion/exclusion criteria, we identified 15 primary studies that discuss software security and software testing. We found approaches, aspects, references, and domains that are used in software security and software testing. Conclusion: We found certain approach, aspect, references, and domain are used more often in software security testing Keywords: Software security, Software testing, Security testing approach, Security threats, Systematic literature review

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

AI technology brought into the field of healthcare is a matter of significant importance as it has contributed to a qualitative improvement of patient care, diagnostics as well as treatment planning. The integrity of the AI-driven healthcare applications including the accuracy, reliability, and safety aspects is what the whole game is about. Even a small matter of software bugs can have severe consequences such as a wrong diagnosis being made, the discharging of patients with the wrong medicines, or a data breach. The most common traditional testing techniques, such as manual testing and rule-based automation, are quite often unsatisfactory as they lack the proper adaptability level that is necessary to cope with the ever-increasing complexity of the newest AI-based healthcare applications. The deployment of AI in software testing has turned out to be an effective method to solve these challenges of ensuring the machine learning algorithm has proper test coverage, defect detection automation, and finally, the healthcare software systems more robust. Automated functional testing, performance testing, security testing, and usability testing are the AI-powered testing methodologies that are the gateway to the development of reliable software. Problematic topics are emphasized in this research such as AI-powered software testing methodologies and their impact on healthcare applications, and the challenges addressing widespread adoption. Forward-thinking is also addressed surrounding the creation of explainable AI (XAI) in testing, continuous integration with DevOps, and AI-powered real-time validation frameworks to ensure the reliability and security of AI-driven healthcare systems.

To create reliable, safe, and high quality software is the primary purpose of software testing. With the development of software engineering, functional software testing is one essential part of software testing. This paper explains that back-propagation neural networks can be used for functional software testing efficiently and significantly. In here we established a method which is sufficient to reveal the majority of software faults in functional testing. We explained how the method can be used to produce a set of test cases covering the most common functional existing in software automatically. Then we used the proposed methodology to test large-scale software systems. Based on the results, in the paper we paid more attention to more arithmetic and date mining methods for function software testing development.

Today, the most vital step is software testing, especially in product quality. Agile software development is exceedingly difficult and has contentious issues in literature and business. Since most Agile techniques place more emphasis on development than software testing activities, different people have varying opinions about how software testing fits into these methodologies. Agile places a high emphasis on frequent deliveries and tight client connections. However, software testing is complex because agile does not incorporate many destructive testing techniques typically required for a product of higher-quality. The study reveals industry-wide Agile testing procedures and the main problems that arise when Agile development is used. Additionally covered are the differences between manual and automated testing, best automation practices, and ways to lead different testing teams in an agile testing environment. This study covers every facet of the Agile software testing process. Agile development has shown that testing is not a distinct stage but rather a crucial component of software development along with the coding. The agile team employs an overall strategy to produce software products of the highest quality. As part of an agile team, testers collect customer samples of the desired behaviour and collaborate with the developer's team to help the coders translate these examples into executable specifications. Coding and testing are done iteratively and incrementally, developing every item until it is valuable enough to release in production. It addresses several testing modalities. The classification provided by its quadrants aids teams in determining and organizing the necessary tests. To increase quality assurance, this article will examine various and improved Agile Testing Processes and methodologies. Additionally, the topic of Agile development's software testing method is covered. Agile development processes can be improved and more advantageous by incorporating testing procedures.

Software free from defects is a goal to be ideally achieved. Defects in software do arise from the development process. A possible solution for quality improvement is to apply a reference framework for software testing during all development phases. What is needed is a software testing process performed along with its development, in an orderly and simple way, even if the software development have been conducted in an organization without maturity certification. This investigation proposes to validate the hypothesis: software product quality is dependent on the software testing. In order to verify and validate the proposed hypothesis, it was applied to a case study, in which the statistical technique for Pearson's Chi-Square was used to determine whether there is a significant relationship among the categorical variables based on software testing results. This case study was derived from an actual project at the Brazilian Aeronautics Institute of Technology and its preliminary results are promising.

Putting Requirements and Quality at the Core of Global Service Delivery: Current Efforts and Future Plans at Pace University

One of the activities that humans always do is communication. This communication is carried out by everyone, but there are special ways of communication that are carried out by people with special needs, such as the deaf. The communication uses sign language, where learning the language is quite difficult, especially for students who are deaf. There is a solution that can solve this problem, namely the use of technology. One of the technologies that can be used is Augmented Reality. Augmented Reality is able to combine the physical world and digital elements that can be used to facilitate human activities. Augmented Reality is designed and packaged in the form of a mobile-based application to make it easier to learn sign language. The use of this designed application is specifically aimed at students with special needs. In addition, the use of the application can also be felt by a group of people who are learning sign language. Application testing involves various aspects, such as functional testing, device compatibility testing, performance testing, AR accuracy and compatibility testing, interaction testing, usability testing, and testing in real environments. App testing engages users, either in user testing or through the feedback they provide. The users involved are divided into 2, namely users as experts in AR testing and users as customers or in this case teachers. The test results prove the value with a high percentage. Tests by experts achieve an average value of 93.13% and tests by customers achieve an average value of 93.60%. This average value can be classified as a reference that the application functions properly, according to needs, is safe, responsive, and provides a satisfying experience.

PurposeThe results after anterior cruciate ligament reconstruction (ACLR) are evaluated by laxity measures, functional tests, and patients’ perception by patient-reported outcome measures (PROMs). It is not known, if one of these evaluation instruments is representative or if outcome scores from all must be reported to obtain a full evaluation of the condition. The aim was to study the correlations between these three types of outcomes 1 year after primary ACLR.MethodAll adult patients (range 18–45 years) who had an ACLR between 1.1.2019 and 31.12.2021 were offered 1-year follow-up by an independent observer. Preoperative information about knee laxity and peroperative information about the condition of menisci and cartilage were registered. At 1-year follow-up clinical and instrumented knee stability and function assessed by four different hop tests were registered. Patients completed four PROMs (the Subjective International Knee Documentation Committee (IKDC) score, the Knee Numeric-Entity Evaluation Score (KNEES-ACL), the Knee injury and Osteoarthritis Outcome Score (KOOS) and the Lysholm score) and Tegner activity scale and answered anchor questions regarding satisfaction and willingness to repeat the operation.ResultsA total of 190 adults attended the 1-year follow-up and 151 had all assessments. There were only a few positive and weak correlations between performance tests and PROMS and between clinical measurements and PROMS (r = 0.00–0.38), and the majority were of negligible strength. Tegner score had in general the highest correlation (low to moderate). The highest correlation was 0.53 (moderate) between the anchor question about patient satisfaction and Lysholm/IKDC scores. There was no difference in the correlations depending on meniscal condition.ConclusionsIn ACLR patients there was no clinically relevant correlation between scores obtained by PROMs, a battery of functional performance tests and instrumented laxity of the knee at 1-year follow-up. Therefore, one type of outcome cannot represent the others. This is an argument for always to include and report all three types of outcomes, and conclusions based on one type of outcome may not be sufficient.Level of evidenceII.

This paper proposes a study of the IPTV world, focusing on network and system architectures, video codecs, network protocols, services and quality assurance. Based on this study, a new framework for Personal TV was developed. This new system is designed primarily to provide new personalized services to the user. In the architecture of this framework there are three main elements: Clients, Aggregators and Producers. The most important element is the aggregator that provides all video contents from the producers to its clients. The client has the possibility of creating his own channel that is sent to the network through its aggregator, of creating customized channels that can be viewed by other clients, among other features. The architecture designed and developed is based on new and studied concepts. It was tested to prove its viability, to assess its performance and to draw conclusions about its scalability, based on functional tests, compatibility tests and performance tests.

BackgroundThe decline in motor function associated with chronic ankle instability (CAI) can be assessed using Functional Performance tests. Ankle muscular strength, endurance and range of motion (ROM) has been assessed in previous studies but functional activities such as sprinting and change of direction are less well studied in athletes with CAI. Hence the aim of this study was to determine how sprint, change of direction, ankle isometric strength, endurance and ROM measures may be associated with discriminate athletes with and without CAI.MethodsOne hundred and six participants (CAI: n = 53 or no CAI: n = 53) provided informed consent to participate in this study. Participants performed three functional performance tests, (30-m sprint test, Modified Illinois change of direction test (MICODT)) and change of direction test. Range of motion for dorsiflexion was measured using weight bearing lunge test and inversion, eversion and plantarflexion using Saunders® digital inclinometer. Strength was assessed using Baseline® hand-held dynamometer for plantarflexors, dorsiflexors, invertors and evertors. Muscular endurance was assessed by single heel raise test and Modified single heel raise test. Between-group comparisons utilised Student’s t-test and Mann-Whitney U-tests, with a number of unique variable and multivariable binomial logistic regression performed to determine which performance measures may discriminate participants with CAI.ResultsThe CAI participants performed significantly worse in the three functional performance tests as well as multiple measures of ankle ROM, isometric strength and muscular endurance (p < 0.008). While several measures of ROM (plantarflexion and dorsiflexion), strength (inversion and eversion) and both muscular endurance tests were significantly associated with CAI in the univariable analysis, the strongest association was the functional performance tests, especially MICDOT time (odds ratio (95% CI): 0.06 [0.02–0.17], sensitivity 94.3%, specificity 88.7%). Multivariable regression analyses indicated that performance across the functional performance tests were more strongly associated with CAI than any ankle ROM, muscular strength or endurance test. Further, the inclusion of the best ankle range of motion, strength or muscular endurance tests did not significantly improve upon the association of the MICDOT with CAI.ConclusionsChronic ankle instability in athletic populations appears to be highly associated with declines in functional performance and to a somewhat lesser extent, ankle range of motion, strength and muscle endurance measures. This may suggest that optimal rehabilitation for athletes with CAI may require a greater focus on improving sprinting speed and change of direction ability in the mid to latter stages of rehabilitation, with regular assessments of these functional performance tests necessary to guide the progression and overload of this training.

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.

Recently, enterprises, organizations, and software companies are building more and more web applications to provide their services over the Internet. In order to fulfill various requirements, the complexity of web applications nowadays is increasing dramatically. As a result, the performance characteristics of web applications, including response time, throughput, etc, become more critical than before and should be taken into careful consideration. If the response time of a web application is poor, users may lose their interests even the function of the web application is correct. Therefore, how to execute performance testing on a complex web application systematically and efficiently will be an important issue. In this paper, a performance testing framework for REST-based web applications is introduced. The performance testing framework aims to provide software testers with an integrated process from test cases design, test scripts generation, to test execution. Based on the test cases designed by software testers and the appropriate software artifacts preserved by the framework (e.g., API document), the framework generates the corresponding performance test scripts, which can be executed by specific performance test tools. This helps software testers to focus more in the design of performance test cases. In addition, effort needed to understand the design and implementation of the application and to learn the operation of testing tools decrease. Thus, the efficiency of performance testing can be highly facilitated.

Although the Unified Parkinson's Disease Rating Scale (UPDRS) is the most common performance measure for people with Parkinson disease (PD), the Berg Balance Scale (BBS), Forward Functional Reach Test (FFR), Backward Functional Reach Test (BFR), Timed "Up & Go" Test (TUG), and gait speed may be used to quantify some aspects of functional performance not measured by the UPDRS. The purpose of this study was to describe the relationship among a set of tests of balance, walking performance, and mobility in people with PD. Twenty-five community-dwelling adults (11 female, 14 male) with a diagnosis of PD were recruited from PD support groups in southeastern Wisconsin and consented to participate in the study. The mean age of the participants was 76 years (SD=7). The average Hoehn and Yahr Stage Scale score was 2. Functional abilities of each subject were assessed with the UPDRS, BBS, FFR, BFR, TUG, and gait speed. Spearman and Pearson correlations were performed. The UPDRS total score was correlated with the BBS (r=-.64, P<.001), FFR (r=-.52, P<.05), and TUG (r=.50, P<.05) measurements. The BBS is the only test of functional performance where scores correlate with all other functional tests and the UPDRS. The UPDRS total score may not reflect a comprehensive measure of mobility in people with PD. Because the BBS scores correlate both with UPDRS scores and with scores of all other tests of functional performance, the BBS appears to be a good overall measure of function in this population.