Software Testing Research Articles

Testability-driven development: An improvement to the TDD efficiency

Test-first development (TFD) is a software development approach involving automated tests before writing the actual code. TFD offers many benefits, such as improving code quality, reducing debugging time, and enabling easier refactoring. However, TFD also poses challenges and limitations, requiring more effort and time to write and maintain test cases, especially for large and complex projects. Refactoring for testability is improving the internal structure of source code to make it easier to test. Refactoring for testability can reduce the cost and complexity of software testing and speed up the test-first life cycle. However, measuring testability is a vital step before refactoring for testability, as it provides a baseline for evaluating the current state of the software and identifying the areas that need improvement. This paper proposes a mathematical model for calculating class testability based on test effectiveness and effort and a machine-learning regression model that predicts testability using source code metrics. It also introduces a testability-driven development (TsDD) method that conducts the TFD process toward developing testable code. TsDD focuses on improving testability and reducing testing costs by measuring testability frequently and refactoring to increase testability without running the program. Our testability prediction model has a mean squared error of 0.0311 and an R2 score of 0.6285. We illustrate the usefulness of TsDD by applying it to 50 Java classes from three open-source projects. TsDD achieves an average of 77.81 % improvement in the testability of these classes. Experts’ manual evaluation confirms the potential of TsDD in accelerating the TDD process.

Investigating the effect of market intelligence activity on the sales strategy and export performance of the study: companies exporting petrochemical products in Shiraz

The purpose of this study is the interaction between market intelligence activities and sales strategy on export performance. The current research is of an applied type and because it investigated and analyzed the current situation, the method used in this research is a descriptive survey. According to the statistical population of the research, 57 companies export petrochemical products according to the information of the Petrochemical Products Exporters Union in Shiraz, the sample size is 170 managers and senior experts, a simple random sampling method was used and the question The letter was distributed among these people. Library and internet sources including books, articles and case studies were used to collect data related to theoretical foundations and extract primary factors and indicators. In inferential statistics, after checking the reliability and validity of the questionnaire, the normality of the data was checked using Kolmogorov Smirnov, and then the assumptions were checked using structural equation modelling test and lisrel software. Based on the obtained results, all hypotheses were accepted. The results of the structural equation model showed that the dissemination of export market information on the performance of the export market, considering the mediating role of prioritizing and targeting the export customer, has the greatest impact with a value of 74%.

Dampak Penerapan Akuntansi Forensik Dan Audit Investigasi Serta Mengevaluasi Proses Pengungkapan Tindak Pidana Fraud Pengadaan Barang/Jasa

As a result of the forensic accounting and investigative audit processes as well as procedures for detecting criminal acts related to the procurement of goods and services, fraud occurred related to community needs as follows: Procurement of goods and services. This usually happens in Indonesia. This research uses the Partial Least Squares (PLS) analysis technique and the SmartPLS 3.2.8 software testing tool. The results show that forensic accounting practices and investigative audit results influence the detection rate of fraud in the procurement of goods and services.

Automated Test Case Generation for Path Coverage by Using Multi-Objective Particle Swarm Optimization Algorithm with Reinforcement Learning and Relationship Matrix Strategies

Software testing is an important phase in software life cycle, and test case generation occupies the main part of the testing workload. Path coverage is the most stringent and difficult coverage criteria, so automatic test case generation for path coverage (ATCG-PC) is an important task in software testing. ATCG-PC is usually abstracted as an optimization search problem, and the commonly used solution algorithm is swarm intelligence algorithm. This paper proposes a path coverage test case generation algorithm based on improved multi-objective particle swarm optimization (IMOPSO) algorithm. The algorithm uses the multi-objective strategy to generate test cases for a group of similar paths at a time, and is supplemented by the particle action selection strategy based on reinforcement learning and the secondary search algorithm based on relationship matrix, which makes use of the similarity information between paths to speed up the early convergence and help to quickly reach the optimal solution around the local optimal position. By grouping the paths according to the similarity, the algorithm reduces the test case consumption, and improves the efficiency of test case generation. Experimental results show that, compared with the existing methods, the proposed algorithm can achieve higher path coverage with less test case consumption and is also suitable for large-scale programs.

Study On the Impact of Hand Exercise on The Cognitive Function of The Elderly Brain

This study aims to evaluate the impact of unarmed strength training on the cognitive function of middle-aged and elderly patients with mild cognitive impairment (MCI). The study subjects are 60 elderly male MCI patients from the Community Health Service Center in Hangzhou, with an age range of 66±5 years. The participants were randomly divided into two groups: the control group and the intervention group. The control group did not receive any intervention measures, while the intervention group underwent a 12-week unarmed strength training program. Before and after the study, the Montreal Cognitive Assessment Scale (MoCA) was used to assess the changes in cognitive function of the subjects. The data analysis was conducted using SPSS 23.0 software, and t-tests and chi-square tests were used for inter-group comparisons based on the distribution characteristics of the data. After 12 weeks of unarmed training, the MoCA score of the intervention group at T2 was significantly higher than that of the control group (P<0.05), indicating that the cognitive function of the intervention group had significantly improved. Unarmed strength training has a positive impact on the cognitive function of middle-aged and elderly MCI patients, and a 12-week training program can effectively improve the cognitive ability of this group.

Balanced Adversarial Tight Matching for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is an attractive research area in software testing. It identifies defects in projects with limited labeled data (target projects) by utilizing predictive models from data-rich projects (source projects). Existing CPDP methods based on transfer learning mainly rely on the assumption of a unimodal distribution and consider the case where the feature distribution has one obvious peak. However, in actual situations, the feature distribution of project samples often exhibits multiple peaks that cannot be ignored. It manifests as a multimodal distribution, making it challenging to align distributions between different projects. To address this issue, we propose a balanced adversarial tight-matching model for CPDP. Specifically, this method employs multilinear conditioning to obtain the cross-covariance of both features and classifier predictions, capturing the multimodal distribution of the feature. When reducing the captured multimodal distribution differences, pseudo-labels are needed, but pseudo-labels have uncertainty. Therefore, we additionally add an auxiliary classifier and attempt to generate pseudo-labels using a pseudo-label strategy with less uncertainty. Finally, the feature generator and two classifiers undergo adversarial training to align the multimodal distributions of different projects. This method outperforms the state-of-the-art CPDP model used on the benchmark dataset.

Depression Conditions of the Elderly Living in Nursing Homes and the Factors Affecting Them

Aim: The aim of our article is to determine the depression status of elderly people living in nursing homes and the affecting factors. Material and Method: The study included 75 elderly individuals who lived in assisted living facilities. Data was collected through a Personal Information Form, the Geriatric Depression Scale, the Mini Mental Assessment Test, and retrospective blood results. The data were analyzed using SPSS 26.0 software and non-parametric tests were used due to the non-normal distribution of the data. The Mann-Whitney U test and the Kruskal-Wallis test were used to compare independent variables. Additionally, multiple regression analysis was employed. Results: The study found that 38.7% of elderly participants had abnormal mini-mental assessments, and 21.3% had significant depression. Statistical analysis revealed significant differences in the total mean scores of the Mini Mental State Test (MMST) based on gender (p=0.023), marital status (p=0.001), educational attainment (p=0.001), and level of schooling. Additionally, there was a statistically significant difference (p=0.002) between the Geriatric Depression Scale (GDS) total mean score and the presence of sleep issues. The total mean scores of MMST indicated a statistically significant difference between the status of urine incontinence (p=0.001) and exercise (p=0.006), as well as their combination. Conclusion: Approximately one third of the participants were depressed and had poor cognitive status. Depression was associated with being female, low educational status and sleep problems, whereas mental status was associated with gender, marital status, and educational status. It is recommended to conduct longitudinal studies with larger samples in future studies.

SCADA implementation in microhydro power plant control and monitoring systems of screw turbine type

Screw turbine-type micro-hydro power plants are still controlled manually by humans when there is a change in the electrical load on the generator output. If there is a change in the electrical load, the generator output voltage will change, thus affecting the rotation of the turbine being used. In this case, humans are needed to manually regulate the flow of water entering the turbine to maintain the stability of the generator output voltage by controlling the sluice gate. The water discharge entering the screw turbine will rotate the generator through a belt/gearbox pulley transmission, thus producing an electrical voltage at the generator output. To maintain the stability of the generator output voltage, it is necessary to control the water discharge entering the screw turbine. The expected goal of this research is to design a control system for a screw turbine type microhydro power plant to stabilize the generator output voltage and monitor the output of a screw turbine type generator using a SCADA (Supervisory Control, and Data Aquisition) system. The method used is the research and development method, namely the system design stage based on secondary data that has been collected, where the system design includes hardware design and software design, followed by making hardware such as control panels and devices. Software, which includes display design and leader diagrams and testing, is carried out by testing the system on software, hardware, and overall system testing. From the results of the discussion and analysis, the screw turbine type microhydro power plant control design system was created, a load control system consisting of 6 groups and sluice gate opening control that uses an ultrasonic sensor to determine the water level. With the results of controlling the sluice gate at a door opening of 30 mm, the generator voltage is 18.42 Volts DC, DC current is 0.0 Ampere at a load of 0 watts, when the load is 30 watts the generator voltage decreases by 18.40 volts, current is 1.37 Ampere. Through the on-line monitoring system, electrical parameters are obtained which are displayed on the SCADA system by looking in real-time at the history of DC voltage and DC current, DC power, and DC energy generated by the screw turbine-type DC generator. The voltage read is 18. 57 volts, current 1.37 Amperes, DC electrical power is 25.4 watts with a DC light load of 30 watts.

Estimating software reliability using size-biased modelling

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.

Histopathological and Molecular Characteristics of Malignant Breast Tumors in the Kurdish Population During 2019 - 2021

Background: Breast cancer, the most prevalent cancer in women and the second leading cause of cancer-related deaths worldwide, is a complex and heterogeneous disease characterized by multiple tumor types with distinct tissue patterns, biological characteristics, and clinical behaviors. Given the critical role of histopathological and molecular characteristics in the treatment and management of breast cancer. Objectives: This study was undertaken to investigate the histopathological characteristics of malignant breast masses in the city of Sanandaj from 2019 to 2021. Methods: In this descriptive-analytical study, out of 1,631 pathology reports from breast tissue samples collected in Sanandaj, a city in northwest Iran, between 2019 and 2021, 597 (36%) malignant cases were examined for their pathological type and molecular characteristics. Data analysis was performed using SPSS 26 software, employing chi-square and Fisher's exact tests. Results: The mean age of the patients at diagnosis was 49.7 ± 11.9 years. The most prevalent molecular subtype was luminal A (56.4%), followed by triple-negative (16.7%), human epidermal growth factor receptor 2 (HER2)-positive/enriched (14.1%), and luminal B (14%). Invasive ductal carcinoma was the predominant histological type, accounting for 82.7% of cases, followed by lobular invasive carcinoma (8.2%). Papillary carcinoma accounted for 5.9% of cases, metaplastic carcinoma for 1.8%, and mucinous carcinoma for 1.3%. Higher grades at the time of diagnosis were associated with the metaplastic subtype (P < 0.001). The only biomarker that showed a significant association with pathological types of breast cancer was Ki-67. The percentage of cases positive for Ki-67 was higher in three types of breast cancer: Infiltrating ductal carcinoma, metaplastic, and papillary (intraductal) carcinoma, compared to the other types (P < 0.001). Conclusions: According to our data, infiltrating ductal and lobular carcinoma are the most prevalent pathological types, and luminal A is the most common molecular subtype among Kurdish women in Iran. It is recommended that future studies examine patient survival based on the pathological and molecular types of tumors, including the evaluation of additional biomarkers. Furthermore, comparing these results with those from other ethnic groups could provide valuable insights into the impact of ethnicity on breast cancer characteristics and outcomes.

Testing Updated Apps by Adapting Learned Models

Although App updates are frequent and software engineers would like to verify updated features only, automated testing techniques verify entire Apps and are thus wasting resources. We present Continuous Adaptation of Learned Models (CALM) , an automated App testing approach that efficiently test App updates by adapting App models learned when automatically testing previous App versions. CALM focuses on functional testing. Since functional correctness can be mainly verified through the visual inspection of App screens, CALM minimizes the number of App screens to be visualized by software testers while maximizing the percentage of updated methods and instructions exercised. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than six state-of-the-art approaches, for the same maximum number of App screens to be visually inspected. Further, in common update scenarios, where only a small fraction of methods are updated, CALM is even quicker to outperform all competing approaches in a more significant way.

Investigating the trend of demographic changes, mortality, clinical and paraclinical findings of patients hospitalized in the Corona ward, before and after the start of general vaccination of COVID-19

BackgroundPrioritizing prevention over treatment has been a longstanding principle in the world health system. This study aims to compare the demographic changes, mortality, clinical, and paraclinical findings of patients hospitalized in the Corona ward before and after the start of general vaccination.MethodsThis cross-sectional study utilized the simple random sampling method in 2022, analyzing 300 medical records of patients admitted to the Corona ward at 22 Bahman Khaf Hospital. Data were collected using a checklist with the help of the Medical Care Monitoring System and analyzed using SPSS-22 statistical software and Chi-square statistical test at a significance level of p < 0.05.ResultsBefore the start of general vaccination for COVID-19, the majority of patients were hospitalized in the Corona Intensive Care Unit (59.3%), aged between 51 and 65 years (47.3%), hospitalized for more than 3 days (54%), required intubation (49.3%), had SPO2 < 93% (60.7%), and exhibited common symptoms such as cough, shortness of breath, and loss of consciousness. Paraclinical findings included positive CRP, decreased lymphocytes, and ground glass opacity (GGO). After the start of general vaccination for COVID-19, most patients were hospitalized in the general care department of Corona (68%), aged between 36 and 50 years (47.3%), hospitalized for less than three days (66%), required intubation (20%), had SPO2 ≥ 93% (77.3%), and exhibited common symptoms such as weakness, headache, and body pain. Paraclinical findings were within the normal range.ConclusionsGeneral vaccination for COVID-19 has significantly reduced patient mortality and morbidity. Health policymakers should prioritize general vaccination to achieve herd immunity and improve public health.

Unit Test Generation Using Large Language Models: A Systematic Literature Review

Unit Test Generation Using Large Language Models: A Systematic Literature Review

A four parameter extension to the Clement matrix and its role in numerical software testing

The Sylvester–Kac matrix (also known as the Clement matrix) and its generalizations are of interest to researchers in diverse fields. A new parameterization of the matrix has recently been presented with closed forms for the eigenvalues and Oste and Van der Jeugt (2017) have proposed their family of matrices as a source of test problems for numerical eigensolvers. In this article we extend their generalization by adding a further two parameters to the matrix definition and, for this new extension, we obtain closed forms for the eigenvalues, determinant and, the left and right eigenvectors. We show that, for certain values of the free parameters and relatively low order, these matrices may become very ill-conditioned w.r.t. both eigenvalues and inversion. In light of this we re-assess the numerical results presented by Oste and Van der Jeugt (2017) and propose a possible new testing role for parameterized Clement matrices.

Enhancing DevOps Engineering Education Through System-Based Learning Approach

Abstract System-based learning (SBL) in engineering domains integrates systems thinking and engineering principles to develop a system. In software engineering, to develop software using the DevOps process, using SBL environment, students gain a comprehensive understanding of the DevOps software development process and apply theoretical concepts to real-world problems by implementing a complete system pipeline, encompassing the design, development, testing, and deployment of software systems. This article introduces an SBL approach to teaching DevOps engineering, addressing the limitations of traditional methods in equipping students with the necessary skills and knowledge. To evaluate the effectiveness of the SBL approach, a case study was conducted to teach a DevOps course within an undergraduate software engineering program. Students completed a project involving the implementation of a system pipeline from requirement gathering to deployment. Results from the case study demonstrate that the SBL approach has improved students’ understanding of DevOps engineering and the software development big picture. The approach enhanced students’ systems thinking and problem-solving capabilities and prepared students for the challenges of a rapidly evolving technological landscape.

Optimization of White Box Testing by Utilizing Branching and Repeating Structures in Java Programs Using Base Path

Software testing is a process created to detect anomalies in the operation of a program or system in order to achieve the expected results. White box testing is a software testing method that tests the internal structure, design and program code. This research aims to produce an optimization method for white box testing in Java programs by utilizing branching and repetition structures using the basis path method, as well as analyzing the effectiveness of the proposed method in generating test cases. The Java program tested in this research includes input function calls, loops, branching, and exception handling. Test case design is carried out by applying the basis path method to achieve comprehensive coverage. The test results show that the base path method is able to produce effective test cases for testing control structures without redundancy. Test case design is assisted by flowgraph and matrix graph modeling. It is hoped that this research can contribute to the optimization of white box testing techniques.

PSIII-6 Tail damage caused by tail biting in growing-finishing pigs housed in two group sizes

Abstract Tail biting can compromise the welfare of pigs and cause economic losses for pork producers, but little is known about the development of tail biting and its resulting impact on pigs. In this study, we investigated victimization by tail biting and the resulting impacts on the welfare and performance of pigs housed in two group sizes. Pigs (n = 432, initial body weight = 22.0 ± 3.7 kg) with intact tails were housed in large (LG = 18 pigs/pen, 12 pens) or small groups (SG = 9 pigs/pen, 24 pens) in a barn with fully slatted floors for 14 wk until market weight (120.7 ± 11.8 kg). Floor space (0.76 m2/pig) and feeder space (4 feeder spaces/9 pigs) allowance were identical between group sizes, and pigs were stratified by initial body weight and sex across treatment pens. Growth performance, pig removal, and tail damage were monitored throughout the study. Tail damage was assessed for each pig weekly and during tail biting outbreaks, using a subjective scoring system (0 = no damage; 1 = healed lesions with small scabs; 2 = puncture wounds with visible blood; 3 = wounds with signs of infection; 4 = partial or total loss of the tail). The maximal tail score (MTS) each pig received every four weeks and over the entire 14 weeks was summarized. Pigs that received MTS of ≥ 2 were considered victimized by a tail biter. Data were analyzed using Glimmix procedure with logit function and FREQ procedure with chi-square and CMH test of SAS software. Compared with SG, LG had greater MTS during the initial 4 wk (1.1 vs. 0.8, chi-square = 17.9, df = 1; P &amp;lt; 0.001) and reduced MTS during the last 2 wk (0.7 vs. 0.9, chi-square = 4.8, df = 1; P = 0.03), suggesting that tail damage declined as pigs grew in LG, but not in SG. The number of pigs removed for tail biting tended to be less and those removed for death and sickness tended to be greater in LG than in SG (chi-square = 7.4; df = 3; P = 0.06). Across group sizes, 68% of the removed pigs (n = 28) were victimized by tail biters (MTS ≥ 2). Between group sizes, there was no difference in time to first victimization or how often individual pigs were victimized throughout the study. Across group sizes, 45% of pigs were never victimized, but 22%, 16%, and 9% were victimized 1, 2, and 3 times, respectively. Eight percent of pigs were victimized more than three times, including 6% with MTS of 3 or 4. Pigs with MTS of 4 over the entire study period were three times more likely (odds ratios between 3.18 and 3.70; all P ≤ 0.03; Table 1) to have market weight lower than the average compared with other pigs. Based on these results, we suggest that management strategies should prevent multiple victimizations or tail loss to maintain pig welfare and performance.

The effect of individual and paired Brailletonik exercises on balance and reaction time in children with intellectual disability

Backgroundchildren with Intellectual Disabilities (ID) are less proficient in motor skills compared to normally developing children, which means they need more time for learning skills. In this context, the purpose of this research is to investigate the effect of the Brailletonik physical activity program (BPAM) on the balance and reaction time of children with ID.MethodsThe statistical sample is consisted of 30 children aged 8 to 12 (21 boys, 9 girls) with ID with an average age of 9.8 ± 1.39, who were selected through convenience sampling. Participants were divided into two groups of individual BPAM, and pair BPAM. Training sessions were held for 21 sessions (seven weeks) and each session included 30 min of training. To measure static balance and reaction time, Stork Stand test and Simple Reaction Time Software were used respectively. The analysis of covariance (ANCOVA) and Bonferroni’s post hoc test were used to analyze the data. Data analysis was performed using SPSS software version 26 at a significance level of p ≤ 0.05.ResultsThe results showed that individual and paired exercise groups had significant progress from pre-test to post-test in both variables of balance and reaction time (p = 0.001). Also, the comparison of the performance of the groups in the post-test showed that the average performance of the paired exercise group was significantly better than the individual exercise group in the balance variable (p = 0.03) and in the reaction time variable (p = 0.01).ConclusionAccordingly, it can be concluded that BPAM in paired groups has a greater effect on the balance and reaction time of children with ID.

Black Box Testing on Website-Based Guestbook Registration Applications

In the rapidly developing digital era, website-based applications have become an important means of meeting the needs of various uses. One application that has received attention is the guest book registration application, which aims to record and manage guest information efficiently. In this context, application testing becomes crucial to ensure service quality, security and availability. This research focuses on black box testing, a software testing method that emphasizes the functionality and reliability of applications from the user's perspective. Testing covers key aspects such as data input, and key functionality. Through this approach, it is hoped that the research results can contribute to the development of a website-based guestbook registration application that is more reliable and suits user needs.

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

Predicting defective software modules before testing is a valuable operation that reduces time and cost of software testing. Source code fault prediction plays a vital role in improving software quality that effectively assists in optimization testing resource allocation. Several machine learning and ensemble learning techniques has been extensively evolved over the recent few years to predict defect at an early stage. These techniques made predictions based on historical defect data, the software metrics. Nevertheless, the time efficient and accurate fault predictions are the major challenging tasks that yet have to be addressed. In order to ensure accurate software fault prediction, a method called Rand-Index Target Projective Gradient Deep Belief Network (RTPGDBN) is designed. The proposed RTPGDBN method comprises of three processes namely acquiring JAVA packages, software metric selection and classification. First, the number of JAVA packages is used as input from the dataset. Second with the JAVA packages obtained as input, Rand similarity indexive target projection function is applied for selecting the most significant software metrics in order to minimize time complexity of fault prediction. Third, with the selected metrics, classification is performed using Tversky Gradient Deep Belief neural network. Also, gradient descent function is applied to minimize classification error, therefore ensuring accurate software fault classification results obtained at the output layer. Experimental setup of proposed RPGDDBN and existing methods are implemented in Java language and the dataset collected from smell prediction replication package. Performance analysis is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity and space complexity. Through extensive experiments on repository data, experimental results indicate that our RPGDDBN method outperforms two state-of-the-art defect detection methods in terms of different performance metrics.