Software Testing Research Articles

An Empirical Study of Key Strategy Factors of Diversity and Inclusion and its Impact on Employees performance in ITES Sector

In this research study the researcher indentify the factors of key strategies of diversity and inclusion at the workplace which significantly enhance the organizational activities such as Leadership Commitment, Training and Education, Inclusive, Policies, Flexible Work Arrangements, Regular Feedback, Diverse Recruitment, Employee Resource Group, Accountability and Measurement. The researcher used the 400 data sample based on the structured questionnaire from the various ITES sectors at different level of employees. This research study is designed with the help of primary and secondary data which are collected through the different research articles and structured questionnaires. For data analysis and interpretation researcher used the SPSS software and Chi-square hypothesis testing tool to test the hypothesis at 0.05 significant levels. The statistical analysis shows that Strongly Disagree 3.8% ,Disagree 11.3% , Neutral 8.8%, Agree 33.0%, Strongly Agree 41.5% are supported to the key strategies for diversity and inclusion at the workplace which significantly enhance the organizational activities. The probability of statistics P-Value is 0.000 which is less than 0.05 significant levels, so the null hypothesis is rejected and result is significant. At this level the researcher concluded that attributes of key strategies for diversity and inclusion at the workplace which significantly enhance the organizational activities.

Optimizing Test Automation with Selenium for Enhancing Web Application Quality

A comprehensive study on Selenium, a widely-used tool for automating web applications for testing purposes, is presented. By examining various methodologies, frameworks, and implementations from existing research, the paper highlights the advantages of Selenium in enhancing software testing efficiency and reliability. The analysis includes case studies on Selenium's application in cloud environments, cross-browser testing, and integration with continuous integration/continuous deployment (CI/CD) pipelines. This work provides a detailed understanding of Selenium's capabilities, challenges, and best practices, contributing to the ongoing advancements in test automation technology

Evaluating the effect of reclaimed asphalt pavement on rubberized hot mix asphalt in pilot projects

The study goal is to evaluate the use of up to 10 % reclaimed asphalt pavement (RAP) by aggregate replacement in rubberized hot mix asphalt-gap graded RHMA-G mixes to identify any significant potential problems for durability. Five pilot projects were built, each including a control RHMA-G (without RAP) and an RHMA-G with 10 % RAP. The mixes were sampled during production and evaluated using volumetric assessment, and performance related tests including stiffness, four-point bending fatigue resistance, fracture resistance measured with the IDEAL cracking test, and rutting resistance. Mix testing results indicate that the addition of 10 % RAP had minor effects on the mechanical properties. With a few exceptions related to the total binder content of the mixes, the effect of the RAP addition was negligible compared to normal production variability. Modeling with CalME, Caltrans mechanistic-empirical asphalt pavement design software and four-point bending testing results, indicated that RAP addition effects on pavement cracking performance were either negligible or comparable to mix-to-mix RHMA-G variability. Regarding constructability, RAP addition did not create any problems. The life cycle assessment completed in the study indicates that the addition of 10 % RAP to the RHMA-G can reduce the greenhouse gasses emissions associated with the RHMA-G production (cradle-to-gate) by up to 5 %.

AbstractTrace: The Use of Execution Traces to Cluster, Classify, Prioritize, and Optimize a Bloated Test Suite

Due to the incremental and iterative nature of the software testing process, a test suite may become bloated with redundant, overlapping, and similar test cases. This paper aims to optimize a bloated test suite by employing an execution trace that encodes runtime events into a sequence of characters forming a string. A dataset of strings, each of which represents the code coverage and execution behavior of a test case, is analyzed to identify similarities between test cases. This facilitates the de-bloating process by providing a formal mechanism to identify, remove, and reduce extra test cases without compromising software quality. This form of analysis allows for the clustering and classification of test cases based on their code coverage and similarity score. This paper explores three levels of execution traces and evaluates different techniques to measure their similarities. Test cases with the same code coverage should generate the exact string representation of runtime events. Various string similarity metrics are assessed to find the similarity score, which is used to classify, detect, and rank test cases accordingly. Additionally, this paper demonstrates the validity of the approach with two case studies. The first shows how to classify the execution behavior of various test cases, which can provide insight into each test case’s internal behavior. The second shows how to identify similar test cases based on their code coverage.

WORK CLIMATE AND ECONOMIC PERFORMANCE IN SUSTAINABLE COMPANIES

This research aimed to investigate the relationship between work climate, sustainability practises and perceived economic performance in sustainable companies, focusing on the impact of Environmental, Social, and Governance (ESG) initiatives. The main objective is to assess how workplace climate influence economic performance and how sustainability practices contribute to employee satisfaction. The study uses a quantitative methodology based on a questionnaire consisting of 15 questions distributed to employees of sustainable companies. The collected data were statistically analysed using SPSS software, with correlation and linear regression tests to validate the hypotheses. Three main hypotheses were tested in the study: (H1) the positive relationship between a favourable work climate and economic performance, (H2) the contribution of sustainability practises to improving work climate, and (H3) the correlation between involvement in ESG initiatives and employee satisfaction. The results indicate a significant correlation between a positive work climate and perceived economic performance as well as a correlation between the implementation of ESG initiatives and employee satisfaction. This research contributes to the understanding of how sustainability practises can influence both employee satisfaction and overall business performance and provides guidance for improving organisational strategies in sustainable companies.

Redefining crowdsourced test report prioritization: An innovative approach with large language model

Context:Crowdsourced testing has gained popularity in software testing, especially for mobile app testing, due to its ability to bring diversity and tackle fragmentation issues. However, the openness of crowdsourced testing presents challenges, particularly in the manual review of numerous test reports, which is time-consuming and labor-intensive. Objective:The primary goal of this research is to improve the efficiency of review processes in crowdsourced testing. Traditional approaches to test report prioritization lack a deep understanding of semantic information in textual descriptions of these reports. This paper introduces LLMPrior, a novel approach for prioritizing crowdsourced test reports using large language models (LLMs). Method:LLMPrior leverages LLMs for the analysis and clustering of crowdsourced test reports based on the types of bugs revealed in their textual descriptions. This involves using prompt engineering techniques to enhance the performance of LLMs. Following the clustering, a recurrent selection algorithm is applied to prioritize the reports. Results:Empirical experiments are conducted to evaluate the effectiveness of LLMPrior. The findings indicate that LLMPrior not only surpasses current state-of-the-art approaches in terms of performance but also proves to be more feasible, efficient, and reliable. This success is attributed to the use of prompt engineering techniques and the cluster-based prioritization strategy. Conclusion:LLMPrior represents a significant advancement in crowdsourced test report prioritization. By effectively utilizing large language models and a cluster-based strategy, it addresses the challenges in traditional prioritization approaches, offering a more efficient and reliable solution for app developers dealing with crowdsourced test reports.

The Role of Artificial Intelligence in Modern Software Engineering

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

Software Quality Assessment Model: A New Approach for Software Testing Tools

The process of software testing is a crucial phase in determining the quality of software, and this phase requires significant costs and a considerable amount of time for testers. This paper discusses the development of a framework for software quality assessment, involving flexible choices of software testing methods and variables in the form of an application. The method used is experimental, developing a new framework based on previous research, where previous research was limited to specific methods and testing variables. The result of this research is the creation of a new framework for software quality assessment. It is hoped that this framework can serve as a reference for software companies in evaluating software quality. In terms of complexity, this framework has the advantage of allowing a tester to choose methods with more flexible or unlimited testing variables. Regarding the estimated time and costs, with [Formula: see text], the practical application complexity of the developed framework is estimated to have the best costs, time and human resources at IDR 254,240,000, with an estimated time of 3,178 work hours and 6,356 work hours with a team of 3 people.

Integrating neural mutation into mutation-based fault localization: A hybrid approach

Fault localization is an important part of software testing and debugging, helping improve the process of fixing faults. Mutation-Based Fault Localization (MBFL) is widely used, but the reliance of Traditional-MBFL on syntactical mutants often limits its accuracy. To address this, we propose Neural-MBFL, which introduces neural mutation to generate semantically richer mutants using deep learning to better mimic real faults. Additionally, we present NeuraIntegra-MBFL, which combines neural and traditional mutation strategies through mutant combination and suspiciousness aggregation. Experiments on 835 faulty programs from the Defects4J benchmark show that Neural-MBFL improves fault localization compared to Traditional-MBFL, with a 35.50% relative improvement in MAP and 127 more faults localized at TOP-5, while maintaining acceptable computational cost. Compared to Neural-MBFL, NeuraIntegra-MBFL further enhances performance, particularly with suspiciousness aggregation, achieving an additional 11.96% MAP improvement and localizes 45 more faults at TOP-5, demonstrating the effectiveness of integrating suspiciousness scores. Using overlap and correlation analyses, we confirmed the complementarity between Neural-MBFL and Traditional-MBFL. Neural-MBFL is more effective at localizing faults that require understanding deep code semantics, while Traditional-MBFL performs better at handling rule-based modifications. NeuraIntegra-MBFL successfully integrates the strengths of both methods, offering better performance than either approach alone.

AI‐Augmented Software Engineering: Revolutionizing or Challenging Software Quality and Testing?

ABSTRACTWith organizations seeking faster, cheaper, and smarter ways of delivering higher quality software, many are looking towards generative artificial intelligence (AI) to drive efficiencies and innovation throughout the software development lifecycle. However, generative AI can suffer from several fundamental issues, including a lack of traceability in concept generation and decision‐making, the potential for making incorrect inferences (hallucinations), shortcomings in response quality, and bias. Quality engineering (QE) has long been utilized to enable more efficient and effective delivery of higher quality software. A core aspect of QE is adopting quality models to support various lifecycle practices, including requirements definition, quality risk assessments, and testing. In this position paper, we introduce the application of QE to AI systems, consider shortcomings in existing AI quality models from the International Organization for Standardization (ISO), and propose extensions to ISO models based on the results of a survey. We also reflect on skills that IT graduates may need in the future, to support delivery of better‐quality AI.

ANXIETY LEVELS AMONG PROFESSIONALS IN THE PSYCHOSOCIAL CARE NETWORK OF PATOS – PB

Anxiety, always present in the evolutionary process of human civilization, is a warning sign that enables people to deal with threats. Among mental disorders, anxiety disorders are the most common and cause considerable functional impairment, constituting one of the main causes of global disability. This study aims to focus on the analysis of the proportion of professionals from the Psychosocial Care Network (RAPS) in Patos-PB who present levels of anxiety, identify triggering factors and suggest strategies to combat these problems. A cross-sectional qualitative and quantitative study was conducted with 39 health professionals from the RAPS in Patos-PB, using questionnaires on social profile and the Hamilton Anxiety Scale (HAM-A). Health professionals with higher education were included, excluding those with previous anxiety disorder or from itinerant teams and NASF. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, with relative and absolute frequency tests, descriptive tests, Pearson's chi-square, Student's t-test and Pearson's correlation. The sample of 39 professionals, mostly female, single, with postgraduate and specialization degrees, revealed that 69.2% of participants reported symptoms of anxiety. The most common coping strategies included physical exercise, deep breathing and psychotherapy. Symptoms such as agitation, irritability, worry, insomnia, palpitations and pain were prevalent. Approximately 82.1% of participants associated work with anxiety, and 66.7% stated that they did not have support from the team or coordination to deal with stress and anxiety in the workplace. In addition, 15.4% presented moderate anxiety and 10.3% intense anxiety. This study reinforces the need for public policies aimed at the mental health of RAPS professionals, promoting a harmonious work environment that can provide qualified and healthy care.

Mobile network and computer software test path selection application in environmental data collection simulation

Mobile network and computer software test path selection application in environmental data collection simulation

Fuzzing trusted execution environments with Rust

Fuzzing, a software testing technique, aims to uncover bugs by subjecting the target program to random inputs, thus discerning abnormal program behaviors such as crashes. In this paper, we present the design and implementation of a fuzzing framework designed to test TEEs (Trusted Execution Environment). Our framework leverages established software tools in a novel way: (1) We employ the Rust programming language in a two-way code generator: to translate fuzzer output to a sequence of system calls and in a “reverse translation” process, where sample code snippets are used to seed the fuzzer – a single API specification suffices for both endeavors; (2) Our fuzzer exhibits the ability to iteratively traverse the API's specification, scrutinize object dependencies, and judiciously reuse objects. These features significantly amplify its bug-finding prowess. (3) A versatile Rust proc macro mechanism is used to process the API specification. The fuzzer's code is built with the Rust compiler sans the necessity for additional specialized tools. (4) To enable the efficient stateful execution of TEEs, we have tailored the QEMU system emulator accordingly. To verify the usability and performance of our fuzzer, and to test various configuration options we conducted a series of tests with a popular open-source OP-TEE trusted operating system.

Co-designing ab initio electronic structure methods on a RISC-V vector architecture

Ab initio electronic structure applications are among the most widely used in High-Performance Computing (HPC), and the eigenvalue problem is often their main computational bottleneck. This article presents our initial efforts in porting these codes to a RISC-V prototype platform leveraging a wide Vector Processing Unit (VPU). Our software tester is based on a mini-app extracted from the ELPA eigensolver library. The user-space emulator Vehave and a RISC-V vector architecture implemented on an FPGA were tested. Metrics from both systems and different vectorisation strategies were extracted, ranging from the simplest and most portable one (using autovectorisation and assisting this by fusing loops in the code) to the more complex one (using intrinsics). We observed a progressive reduction in the number of vectorised instructions, executed instructions and computing cycles with the different methodologies, which will lead to a substantial speed-up in the calculations. The obtained outcomes are crucial in advancing the porting of computational materials and molecular science codes to (post)-exascale architectures using RISC-V-based technologies fully developed within the EU. Our evaluation also provides valuable feedback for hardware designers, engineers and compiler developers, making this use case pivotal for co-design efforts.

Enhancing software defect prediction models using metaheuristics with a learning to rank approach

In today’s world, software systems are extensively used across industries like medicine, commerce, education, entertainment, and industry. The development process includes design, development, and testing, where software testing is vital for quality assurance. Manual testing is laborious and resource-heavy, especially in large projects. Researchers are investigating methods to predict software defects by analyzing source code metrics, with Machine Learning techniques showing promise. The Machine learning models typically employs three approaches to predict defects: presence, number, and density. One cutting-edge approach in such models is LTR (Learning To Rank), which considers the order and values of predictions, aiding enterprises in efficiently allocating resources to more defective components. This article aims to enhance the performance of 5 machine learning models by leveraging 5 metaheuristics to optimize their hyperparameters. The study was conducted using 28 common datasets from the literature, with the FPA metric as the evaluation criteria. The results demonstrate that the proposed method achieved a 13% increase compared to state-of-the-art models with Random Forest, achieving an average FPA of 0.84 across the datasets.

SafeTI: a Hardware Traffic Injector for Complex MPSoC Platform Validation and Characterization

Functional and timing validation of high performing safety-related platforms requires testing specific traffic patterns in the network-on-chip interconnects. Generally, testing needs to be performed by using software tests whose degree of control on the traffic generated is indirect, and limited to behavior that can be triggered by software, thus often unable to produce traffic generated by peripherals. Therefore, untested traffic scenarios can be abundant and, to a large extent, it is hard to know what traffic scenarios have been effectively tested. This paper presents the safe traffic injector , SafeTI, which allows injecting programmable traffic in AMBA AHB interconnects with high flexibility and degree of control, thus easing achieving high coverage in terms of traffic scenarios tested, and mitigating the uncertainty due to the difficulties to relate software tests with actual traffic scenarios tested. We also integrate successfully the SafeTI in the safety open platform SELENE proving the effectiveness of the proposed traffic injector for characterizing the platform memory access capabilities under different patterns of contention on the multi-level cache system.

The role of mir-214-5p and mir-548-5p expressions in endometriosis.

Endometriosis is a benign gynecological disease that affects about 1% of all women and up to 15% of women of childbearing age. To date, none of the proposed theories exhaustively explain the pathophysiology of the disease or the associated clinical manifestations. As part of efforts to introduce new methods for the early and non-invasive diagnosis of endometriosis, this project investigated changes in the expression of miR-214-5p and miR-548-5p in ectopic and eutopic tissue compared to normal endometrial tissue. Forty-five samples (15 eutopic, 15 ectopic and 15 healthy controls) from women referred to Shahid Sadoughi Hospital (Yazd, Iran). RNA extraction was performed using an RNA extraction kit, and cDNA was synthesized. Two-step qRT-PCR was performed according to the manufacturer's instructions. GraphPad Prism 8 software and Two-Way ANOVA test were used to compare fold-change expression. The results indicate a significant down-regulation of miR-214-5p expression levels (P-value < 0.05) and an increase in miR-548-5p expression levels (P-value < 0.05) in endometriosis samples compared to those in control tissues. miR-214-5p and miR-548-5p may regulate the pathogenesis of endometriosis. The down-regulation of miR-214-5p in people with endometriosis compared to healthy individuals may indicate its suppressive role. The upregulation of miR-548-5p could confirm the oncogenic role of this microRNA in endometriosis. The development of new therapeutic strategies targeting these miRNAs could be promising in the treatment of this disease.

Generation of algebraic data type values using evolutionary algorithms

Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalize the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques.

AI-Powered Test Automation for Financial Cloud Applications Using LLMs and Serverless Computing

This article proposes a conceptual framework for AI-powered testing of financial cloud applications by integrating Large Language Models (LLMs) with serverless computing architecture. The framework addresses critical challenges in financial software testing, including regulatory compliance, security validation, and performance scalability. Through theoretical analysis, the design incorporates sophisticated prompt engineering techniques and automated compliance monitoring systems to achieve high compliance validation accuracy while potentially reducing monitoring costs. The architecture suggests superior performance capabilities, potentially processing concurrent test executions efficiently while reducing operational costs compared to traditional testing infrastructures. The framework design anticipates improvements in test generation efficiency, regulatory compliance coverage, and overall testing costs while maintaining robust security standards through enhanced vulnerability detection mechanisms. The theoretical integration of LLMs with serverless computing shows promise in addressing the complex challenges of financial software testing, offering a potential pathway to a scalable, cost-effective solution that could meet the stringent requirements of modern financial cloud applications while ensuring regulatory compliance and security.

Executive Function and young children's Cardinality Principle: the mediating role of the Approximate Number System and the moderating role of age.

Executive Function and the Approximate Number System are well-established as critical components in developing the Cardinality Principle in young children. However, most existing studies explore the relationship between these variables in isolation without examining whether Approximate Number System mediates the relationship between Executive Function and the Cardinality Principle and the role of age in this. This study aimed to address this gap by investigating the mediating role of the Approximate Number System in the relationship between Executive Function and the Cardinality Principle and the moderating role of age in young children. This cross-sectional study was conducted in China from February to June 2024. A total of 203 young children (97 boys and 106 girls, Mean age = 68.93 ± 7.076 months) participated. Participants were assessed using a range of tests: the Day-Night Stroop Task, Digit Recall Task, Dimensional Change Card Sort Task, Panamath Test Software, How Many Task, and Give-N Task to measure Executive Function, Approximate Number System, and Cardinality Principle. Data were analyzed using SPSS 26.0 and PROCESS v4.1 (Model 4) to explore the relationships among Executive Function, the Approximate Number System, and the Cardinality Principle through Pearson correlations, multivariate regression, and mediation analysis with 5000 bootstrap samples. Correlation analysis revealed that the Cardinality Principle was significantly and positively correlated with Inhibitory Control, Working Memory, Cognitive Flexibility, Executive Function, and the Approximate Number System. Regression analyses indicated that Executive Function positively predicted young children's Cardinality Principle. Specifically, Working Memory and Cognitive Flexibility were positive predictors of the Cardinality Principle, while Inhibitory Control was not. Mediation analysis results demonstrated that the Approximate Number System mediated the relationships between Inhibitory Control and the Cardinality Principle, Working Memory and the Cardinality Principle, and Cognitive Flexibility and the Cardinality Principle, respectively. In addition, the study found that young children's age negatively moderated the relationship between the Approximate Number System and the Cardinality Principle. The study emphasizes that in developing young children's Cardinality Principle, emphasis should be placed on improving their Executive Function and Approximate Number System while considering the age differences of young children and developing appropriate educational methods for different age groups.