Reliability Of Software Products Research Articles

Numerical and Symbolic Analysis for Mathematical Problem-Solving with Maple

This study explores the versatile capabilities of Maple, a widely used mathematical software, in addressing a wide range of numerical and symbolic computations essential for scientific and engineering applications. The researchers investigated Maple's diverse suite of tools, including numerical integration, nonlinear equation solving, polynomial interpolation, symbolic integration, and various numerical methods. Through an in-depth literature review, illustrated case studies, and detailed performance evaluations, the paper demonstrates the effectiveness and accuracy of Maple's computational approaches in dealing with complex problems in various areas of applied mathematics. This study's findings underscored Maple's tremendous value as a reliable and comprehensive software package for researchers, scientists, and professionals involved in advanced mathematical analysis and scientific computing. Furthermore, the paper highlighted Maple's versatility in creating high-quality three-dimensional plots, crucial for visualizing and analyzing complex mathematical and scientific data. Using either sets or lists, the ability to display multiple surfaces in a single three-dimensional plot showcases Maple's power in data visualization and communicating complex ideas. By positioning Maple as a powerful platform for solving versatile mathematical problems, this study highlights the software's indispensable role in advancing scientific discoveries and engineering innovations.

Continuous Integration and Delivery of Software products: Pipeline implementation

Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. CI/CD is the best practice for teams using a DevOps methodology and is best suited to agile methodologies. CI/CD automates code integration and delivery, allowing software development teams to focus more on business requirements. Implementing CI/CD and automating the delivery process from application development to the live environment using pipelines, reducing the time and errors during manual delivery, the previously mentioned high-quality and reliable software products are delivered to the end user. This work will automate the workflow of software products from development to supply and delivery using pipelines.

Software Test Case Generation Tools and Techniques: A Review

Software Industry is evolving at a very fast pace since last two decades. Many software developments, testing and test case generation approaches have evolved in last two decades to deliver quality products and services. Testing plays a vital role to ensure the quality and reliability of software products. In this paper authors attempted to conduct a systematic study of testing tools and techniques. Six most popular e-resources called IEEE, Springer, Association for Computing Machinery (ACM), Elsevier, Wiley and Google Scholar to download 738 manuscripts out of which 125 were selected to conduct the study. Out of 125 manuscripts selected, a good number approx. 79% are from reputed journals and around 21% are from good conference of repute. Testing tools discussed in this paper have broadly been divided into five different categories: open source, academic and research, commercial, academic and open source, and commercial & open source. The paper also discusses several benchmarked datasets viz. Evosuite 10, SF100 Corpus, Defects4J repository, Neo4j, JSON, Mocha JS, and Node JS to name a few. Aim of this paper is to make the researchers aware of the various test case generation tools and techniques introduced in the last 11 years with their salient features.

On a cost and availability analysis for software systems via phase type non-homogeneous Poisson process

To build highly reliable software products, software testing is required. The software engineer has a problem determining whether to complete the testing process and when to release the software system to the market. As the focus on high software increases, project managers must consider the cost of testing, testing availability time, and release time strategy. This research article utilizes a phase type non-homogeneous Poisson process to investigate the cost analysis and operational availability of a software reliability model. The software failure inter-arrival time is considered to follow a phase type distribution. The phase type software reliability model is valuable in reducing the time and effort required to select the appropriate models for software reliability evaluation. An explicit expression for the expected, bi-criterion cost analysis and operational availability is derived using standard results of the renewal reward theorem. The optimal software release policy T* is obtained analytically. Numerical illustrations and sensitivity analysis have been presented to illustrate the cost and availability analysis’s efficiency and demonstrate conformity with the study’s observations.

Computerized Study and Analysis Electrical Power Systems Parameters

In this paper, a program for analyzing potential events and designing preventive measures to ensure safe operation of electrical power systems was developed, which was published in the Journal of Al-Baath University. The new visual software system performs all the functions of the previous program, in addition to a set of developed options, studies and comparisons. It also features a graphical method for entering electrical network data and conforms to software engineering standards, with an analytical study based on an approved methodology, the Unified Modeling Language (UML). The spiral software model was chosen during the research, which made it possible to obtain a reliable software product that is characterized by the highest possible standards. In this paper, the parameters of the electrical power system on a 6-busbar network were analyzed and studied, and the effect of changing the power factor on the possibility of finding the optimal solution, whether by changing generation or by changing generation and decreasing loads, was studied. The program's effectiveness, flexibility and accuracy of results have been proven.

Alternative Simplified Analytical Models for the Electric Field, in Shoreline Pond Electrode Preliminary Design, in the Case of HVDC Transmission Systems

In Greece, a new bi-polar high voltage direct current (HVDC) transmission system with a ground return was designed with nominal characteristics of ±500 kV, 1 GW, between Attica in the continental country and the island of Crete, which is an autonomous power system based on thermal diesel units. The interconnection line has a total length of about 380 km. The undersea section is 330 km long. In this paper, the use of the Aegean Sea as an active part of the ground return, based on shoreline pond electrodes, was proposed to avoid EUR 200 Μ of expenses. According to the general guidelines for HVDC electrode design by the International Council on Large Electric Systems (CIGRE) working group B4.61/2017, the electric field and ground potential rise of shoreline electrodes should be studied to analyze safety, electrical interference and corrosion impacts related to the operation of the electrodes. Two kinds of studies are available; one is a simplified approach based on a spherical/pointed electrode centered at the edge of the seashore and seabed, assuming it to be sloping to the horizontal, and the other is a detailed simulated model using a suitable electric field software package. The first approach usually gives more unfavorable results than the second one, especially in the near electric field, while it can not take into account obstacles, i.e., dams, near to electrode position. The second approach demands a detailed description of the wider installation area, which cannot be available during the preliminary study, significant computational time and considerable financial resources for the purchase of a reliable specialized software package. In this research, a two-step modification of the CIGRE simplified model was proposed. The first modification deals with the obstacles in the near electric field, and the second modification deals with the use of a linear current source (instead of a point one), which can give more accurate results. Additionally, the electric field for complex electrode formation is calculated by applying the superposition method, which can be easily achieved using a common software package, i.e., MATLAB. The proposed simplified approaches were applied on shoreline pond electrode locations for the Attica–Crete HVDC interconnection line (between Stachtoroi island in Attica and Korakia beach in Crete), allowing the preliminary study to be conducted swiftly, giving satisfactory results about electric field gradient, ground potential rise and resistance to remote earth of electrodes stations for the near and far electric field.

A New Learning to Rank Approach for Software Defect Prediction

Software defect prediction is one of the most active research fields in software development. The outcome of defect prediction models provides a list of the most likely defect-prone modules that need a huge effort from quality assurance teams. It can also help project managers to effectively allocate limited resources to validating software products and invest more effort in defect-prone modules. As the size of software projects grows, error prediction models can play an important role in assisting developers and shortening the time it takes to create more reliable software products by ranking software modules based on their defects. Therefore, there is need a learning-to-rank approach that can prioritize and rank defective modules to reduce testing effort, cost, and time. In this paper, a new learning to rank approach was developed to help the QA team rank the most defect-prone modules using different regression models. The proposed approach was evaluated on a set of standardized datasets using well-known evaluation measures such as Fault-Percentile Average (FPA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Cumulative Lift Chart (CLC). Also, our proposed approach was compared with some other regression models that are used for software defect prediction, such as Random Forest (RF), Logistic Regression (LR), Support Vector Regression (SVR), Zero Inflated Regression (ZIR), Zero Inflated Poisson (ZIP), and Negative Polynomial Regression (NPR). Based on the results, the measurement criteria were different than each other as there was a gap in the accuracy obtained for defects prediction due to the nature of the random data, and thus was higher for RF and SVR, as well as FPA achieved better results than MAE and RMSE in this research paper.

Multicriteria Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression for software quality management

The paper presents the software quality management is a highly significant one to ensure the quality and to review the reliability of software products. To improve the software quality by predicting software failures and enhancing the scalability, in this paper, we present a novel reinforced Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression (RCSOLDA-RIR) technique. At first, Multicriteria reinforced Cuckoo search optimization is used to perform the test case selection and find the most optimal solution while considering the multiple criteria and selecting the optimal test cases for testing the software quality. Next, the generative latent Dirichlet allocation model is applied to predict the software failure density with selected optimal test cases with minimum time. Finally, the Ruzchika indexive regression is applied for measuring the similarity between the preceding versions and the new version of software products. Based on the similarity estimation, the software failure density of the new version is also predicted. With this, software error prediction is made in a significant manner, therefore, improving the reliability of software code and service provisioning time between software versions in software systems is also minimized. An experimental assessment of the RCSOLDA-RIR technique achieves better reliability and scalability than the existing methods.

Software reliability prediction and release time management with coverage

PurposeSoftware testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested.Design/methodology/approachThe fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables.FindingsThe practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches.Originality/valueThis study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testing duration.

Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment.

Automating cardiac function assessment on cardiac magnetic resonance short-axis cines is faster and more reproducible than manual contour-tracing; however, accurately tracing basal contours remains challenging. Three automated post-processing software packages (Level 1) were compared to manual assessment. Subsequently, automated basal tracings were manually adjusted using a standardized protocol combined with software package-specific relative-to-manual standard error correction (Level 2). All post-processing was performed in 65 healthy subjects. Manual contour-tracing was performed separately from Level 1 and 2 automated analysis. Automated measurements were considered accurate when the difference was equal or less than the maximum manual inter-observer disagreement percentage. Level 1 (2.1 ± 1.0 min) and Level 2 automated (5.2 ± 1.3 min) were faster and more reproducible than manual (21.1 ± 2.9 min) post-processing, the maximum inter-observer disagreement was 6%. Compared to manual, Level 1 automation had wide limits of agreement. The most reliable software package obtained more accurate measurements in Level 2 compared to Level 1 automation: left ventricular end-diastolic volume, 98% and 53%; ejection fraction, 98% and 60%; mass, 70% and 3%; right ventricular end-diastolic volume, 98% and 28%; ejection fraction, 80% and 40%, respectively. Level 1 automated cardiac function post-processing is fast and highly reproducible with varying accuracy. Level 2 automation balances speed and accuracy.

Leadership approach towards Agile, Waterfall and Iterative implementation of the software development products

The automotive industry is one of the most dynamic markets, gathering around many different sectors from material extractions to robotics and cloud computing. The current research analyses the influences and particularities of leadership and identifies the challenges and advantages of the two principal methodologies in software development projects within the automotive industry, Agile and Waterfall. Some aspects of reducing methodological disadvantages are suggested and presented. The aim is to arrive at a sustainable solution that can ensure the delivery of reliable and flexible software products. The impact of these methodologies on the quality standards and project documentation is analyzed, which is a vital part of automotive software development. By influencing the product development lifecycle, there is an opportunity to develop a structured framework that can be adopted to build software products in a more flexible and agile way.

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Test Case Quality Factors: Content Analysis of Software Testing Websites

Software testing is anessentialprocess for ensuring thequality and reliability of software products. The efficiency of testing activities depends largely on the test case quality, which is considered as one of the major concerns of software testing. Unfortunately, at the moment there is no clear guideline that can be referred by software testers in producing good quality test cases. Hence, producing guideline is certainly required. To construct a pragmatic guideline, it is crucial to identify the factors that lead todesigninggood quality test cases. The existing test case quality factors are not comprehensive and need further investigation and improvement. Therefore,a content analysis was conducted to identify the test case qualityfactors from software testing experts point of view available in the software testing websites. The software testing websites provide explicit information about the quality of test cases in order to avoid the poor design of test cases. Thus, this study presents the outcomes of content analysis from 22 software testing websites which comprise of static content websites and blogs.Consequently, eight (8)factors and their corresponding 30 sub-factors were identified. Among the factors are documentation, manageability, maintainability, reusability, requirement quality, efficiency, tester knowledge, and effectiveness of test cases. These factors are useful to be referred by the practitioners in assuring the quality of the design test cases which implicitly can ensure the quality of the software products.

Survey on software defect prediction techniques

ABSTRACT Recent advancements in technology have emerged the requirements of hardware and software applications. Along with this technical growth, software industries also have faced drastic growth in the demand of software for several applications. For any software industry, developing good quality software and maintaining its eminence for user end is considered as most important task for software industrial growth. In order to achieve this, software engineering plays an important role for software industries. Software applications are developed with the help of computer programming where codes are written for desired task. Generally, these codes contain some faulty instances which may lead to the buggy software development cause due to software defects. In the field of software engineering, software defect prediction is considered as most important task which can be used for maintaining the quality of software. Defect prediction results provide the list of defect-prone source code artefacts so that quality assurance team scan effectively allocate limited resources for validating software products by putting more effort on the defect-prone source code. As the size of software projects becomes larger, defect prediction techniques will play an important role to support developers as well as to speed up time to market with more reliable software products. One of the most exhaustive and pricey part of embedded software development is consider as the process of finding and fixing the defects. Due to complex infrastructure, magnitude, cost and time limitations, monitoring and fulfilling the quality is a big challenge, especially in automotive embedded systems. However, meeting the superior product quality and reliability is mandatory. Hence, higher importance is given to V&V (Verification & Validation). Software testing is an integral part of software V&V, which is focused on promising accurate functionality and long-term reliability of software systems. Simultaneously, software testing requires much effort, cost, infrastructure and expertise as the development. The costs and efforts elevate in safety critical software systems. Therefore, it is essential to have a good testing strategy for any industry with high software development costs. In this work, we are planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.

Testing coverage based reliability modeling for multi-release open-source software incorporating fault reduction factor

Testing represents an important and necessary phase in the development process of a software that provides a reliable and efficient software product. To enhance software reliability, fault detection and correction are carried out during this process. In order to develop highly reliable software system software reliability growth models (SRGMs) are observed to be the most effective way. These SRGMs have been developed under a set of assumptions, there are many real-life factors that control the reliability of the software system during the testing process. In that context Fault Reduction Factor (FRF) and testing coverage are the most important key factors. In our proposed study we have developed SRGMs based on these environmental factors. We have also discussed the concept of multi releases of an Open-Source Software (OSS) project as a single release of the product may not meet the ever-increasing requirements of a competitive market. Therefore, this paper talks about the impact of different parameters such as FRF and testing coverage on the reliability of the different releases of an OSS. The mathematical expression based on these assumptions is provided by two models. These models assume the FRF as constant and testing coverage is assumed to have Weibull and Exponentiated Weibull distribution functions. The proposed models are then validated on real-life faults datasets and their performance is measured on the basis of different criteria of the goodness of fit.

Test-Case Minimization Technique for Large Scale Software using Link-Matrix

In today’s software development environment, testing commences just after the finalization of systems requirements. Testing process is executed to make the underlying software error free, to enhance and assure the development of reliable and quality software product. This paper explores and analyses some of black-box as well as white-box testing methods according to their key findings, measures and metrics and considerations of testing factors. Further this paper proposes an efficient test case minimization technique. This technique uses Link-Matrix to identify the number of actual parameters used between components. We have used Boundary Value Analysis (BVA) method to compute the count of tests cases for individual modules or components. We have compared the proposed technique with all three cases of Boundary Value Analysis method including normal case, robust case and worst case. Results achieved during comparison shows that this proposed technique is effective to minimize the total count of test-cases especially in case of robust and worst scenarios.

Supervised machine learning approach to predict qualitative software product

Software development process (SDP) is a framework imposed on software product development and is a multi-stage process wherein a wide range of tasks and activities pan out in each stage. Each stage requires careful observations to improve productivity, quality, etc. to ease the process of development. During each stage, problems surface likes constraint of on-time completion, proper utilization of available resources and appropriate traceability of work progress, etc. and may lead to reiteration due to the defects spotted during testing and then, results into the negative walk-through due to unsatisfactory outcomes. Working on such defects can help to take a step towards the proper steering of activities and thus to improve the expected performance of the software product. Handpicking the proper notable features of SDP and then analyzing their nature towards the outcome can greatly help in getting a reliable software product by meeting the expected objectives. This paper proposed supervised Machine Learning (ML) models for the predictions of better SDP, particularly focusing on cost estimation, defect prediction, and reusability. The experimental studies were conducted on the primary data, and the evaluation reveals the model suitability in terms of efficiency and effectiveness for SDP prediction (accuracy of cost estimation: 65%, defect prediction: 93% and reusability: 82%).

Fusion of intra-oral scans in cone-beam computed tomography scans

PurposeThe purpose of this study was to evaluate the clinical accuracy of the fusion of intra-oral scans in cone-beam computed tomography (CBCT) scans using two commercially available software packages.Materials and methodsTen dry human skulls were subjected to structured light scanning, CBCT scanning, and intra-oral scanning. Two commercially available software packages were used to perform fusion of the intra-oral scans in the CBCT scan to create an accurate virtual head model: IPS CaseDesigner® and OrthoAnalyzer™. The structured light scanner was used as a gold standard and was superimposed on the virtual head models, created by IPS CaseDesigner® and OrthoAnalyzer™, using an Iterative Closest Point algorithm. Differences between the positions of the intra-oral scans obtained with the software packages were recorded and expressed in six degrees of freedom as well as the inter- and intra-observer intra-class correlation coefficient.ResultsThe tested software packages, IPS CaseDesigner® and OrthoAnalyzer™, showed a high level of accuracy compared to the gold standard. The accuracy was calculated for all six degrees of freedom. It was noticeable that the accuracy in the cranial/caudal direction was the lowest for IPS CaseDesigner® and OrthoAnalyzer™ in both the maxilla and mandible. The inter- and intra-observer intra-class correlation coefficient showed a high level of agreement between the observers.Clinical relevanceIPS CaseDesigner® and OrthoAnalyzer™ are reliable software packages providing an accurate fusion of the intra-oral scan in the CBCT. Both software packages can be used as an accurate fusion tool of the intra-oral scan in the CBCT which provides an accurate basis for 3D virtual planning.

Software reliability prediction and management: A multiple change‐point model approach

AbstractIt is commonly recognized that software development is highly unpredictable and software quality may not be easily enhanced after software product is finished. During the software development life cycle (SDLC), project managers have to solve many technical and management issues, such as high failure rate, cost over‐run, low quality, and late delivery. Consequently, in order to produce robust and reliable software product(s) on time and within budget, project managers and developers have to appropriately allocate limited time, manpower, development, and testing effort. In the past, the distribution of testing effort or manpower can typically be described by the Weibull or Rayleigh model. Practically, it should be noticed that development environments or methods could be changed due to some reasons. Thus, when we plan to perform software reliability modeling and prediction, these changes or variations occurring in the development process have to be taken into consideration. In this paper, we will study how to use the Parr‐curve model with multiple change‐points to depict the consumption of testing effort and how to perform further software reliability analysis. Some mathematical properties of proposed model will be given and discussed. The applicability and performance of our proposed model will be demonstrated and assessed through real software failure data. Experimental results are analyzed and compared with other existing models to show that our proposed model gives better predictions. Finally, an optimal software release policy based on cost‐reliability criteria is proposed and studied. The main purpose is aimed at minimizing the total cost of software development when a reliability objective is given.

Reliability Assessment of Software System Using IFS and OWA-Tree Analysis

To address the software design and development, reliability assessment is considered as crucial and most important task. Several studies have been directed towards reliability assessment approaches for obtaining highly reliable software product. In conventional reliability theory, failure probability of any component is assumed as an exact value but in actuality it’s not possible to get failure probability precisely. In this study, we have proposed an approach to assess the reliability of a software system with vague failure rate of the components as the given information might be incomplete or uncertain. It is a bottom–top methodology which includes the combination of intuitionistic fuzzy set (IFS) theory and ordered weighted averaging (OWA) tree analysis. Using IFS, we are able to come over the vagueness in the failure rate data and by using OWA-tree, we incorporate the subjectivity in the opinion of software developers with respect to selection of module. Further, for the illustration of the proposed approach one numerical example has been discussed and software reliability is assessed based upon different orness level.