Software Release Research Articles

Software Belief Reliability Growth Model Based on Uncertain Differential Equation

Software reliability plays an important role in modern society. To evaluate software reliability, software reliability growth models (SRGMs) investigate the number of software faults in the testing phase. Obviously, testing progresses are inevitably influenced by dynamic indeterministic fluctuations such as the testing effort expenditure, testing efficiency and skill, testing method, and strategy. To model these dynamic fluctuations, several probability theory-based SRGMs are proposed. However, probability theory is suitable for dealing with aleatory uncertainty, but fails to deal with epistemic uncertainty widely existing in software faults. Therefore, this article considers software reliability from a new perspective under the framework of uncertainty theory, which is a new mathematical system different from probability theory, and proposes a software belief reliability growth model (SBRGM) based on uncertain differential equations for the first time. Based on this SBRGM, properties of essential software reliability metrics are investigated under belief reliability theory, which is a brand-new reliability theory. Parameter estimations for unknown parameters in SBRGM are presented. Furthermore, some numerical examples and real data analyses illustrate our methodology in detail, and show that it performs better than several famous probability-based SRGMs in terms of fitting ability and prediction ability. Finally, an optimal software release policy is discussed.

Software Release Assessment under Multiple Alternatives with Consideration of Debuggers’ Learning Rate and Imperfect Debugging Environment

In the software development life cycle, the quality and reliability of software are critical to software developers. Poor quality and reliability not only cause the loss of customers and sales but also increase the operational risk due to unreliable codes. Therefore, software developers should try their best to reduce such potential software defects by undertaking a software testing project. However, to pursue perfect and faultless software is unrealistic since the budget, time, and testing resources are limited, and the software developers need to reach a compromise that balances software reliability and the testing cost. Using the model presented in this study, software developers can devise multiple alternatives for a software testing project, and each alternative has its distinct allocation of human resources. The best alternative can therefore be selected. Furthermore, the allocation incorporates debuggers’ learning and negligent factors, both of which influence the efficiency of software testing in practice. Accordingly, the study considers both human factors and the nature of errors during the debugging process to develop a software reliability growth model to estimate the related costs and the reliability indicator. Additionally, the issue of error classification is also extended by considering the impacts of errors on the system, and the expected time required to remove simple or complex errors can be estimated based on different truncated exponential distributions. Finally, numerical examples are presented and sensitivity analyses are performed to provide managerial insights and useful directions to inform software release strategies.

Bayesian Decision Making of an Imperfect Debugging Software Reliability Growth Model with Consideration of Debuggers’ Learning and Negligence Factors

In this study, an imperfect debugging software reliability growth model (SRGM) with Bayesian analysis was proposed to determine an optimal software release in order to minimize software testing costs and also enhance the practicability. Generally, it is not easy to estimate the model parameters by applying MLE (maximum likelihood estimation) or LSE (least squares estimation) with insufficient historical data. Therefore, in the situation of insufficient data, the proposed Bayesian method can adopt domain experts’ prior judgments and utilize few software testing data to forecast the reliability and the cost to proceed with the prior analysis and the posterior analysis. Moreover, the debugging efficiency involves testing staff’s learning and negligent factors, and therefore, the human factors and the nature of debugging process are taken into consideration in developing the fundamental model. Based on this, the estimation of the model’s parameters would be more intuitive and can be easily evaluated by domain experts, which is the major advantage for extending the related applications in practice. Finally, numerical examples and sensitivity analyses are performed to provide managerial insights and useful directions for software release strategies.

Real-time structure search and structure classification for AlphaFold protein models

Last year saw a breakthrough in protein structure prediction, where the AlphaFold2 method showed a substantial improvement in the modeling accuracy. Following the software release of AlphaFold2, predicted structures by AlphaFold2 for proteins in 21 species were made publicly available via the AlphaFold Database. Here, to facilitate structural analysis and application of AlphaFold2 models, we provide the infrastructure, 3D-AF-Surfer, which allows real-time structure-based search for the AlphaFold2 models. In 3D-AF-Surfer, structures are represented with 3D Zernike descriptors (3DZD), which is a rotationally invariant, mathematical representation of 3D shapes. We developed a neural network that takes 3DZDs of proteins as input and retrieves proteins of the same fold more accurately than direct comparison of 3DZDs. Using 3D-AF-Surfer, we report structure classifications of AlphaFold2 models and discuss the correlation between confidence levels of AlphaFold2 models and intrinsic disordered regions.

Decision Making of Software Release Time at Different Confidence Intervals with Ohba’s Inflection S-Shape Model

Software developers need information for deciding the optimal time for software release with improved software reliability. However, it is not easy for them to decide when and how to release newly developed software to the market. For a decision, the reliability and test costs of the software need to be balanced carefully for avoiding unnecessary confusion and users’ complaints. To address this need, related research has been carried out to propose an appropriate tool for such decisions. In many studies, software reliability growth models (SRGMs) were applied using the concept of confidence intervals to estimate the reliability of software. Confidence intervals were calculated on the basis of the assumption of a normal distribution showing the symmetrical occurrence of data with the mean as a center. However, the reliability data of software do not always have such symmetry for assuming the normal distribution. Therefore, it is necessary to propose a method for overcoming the mean value randomness that causes asymmetry in the related data. In previous studies, estimating variance and mean of errors of software was not considered, which led to the unreliable estimation of the confidence intervals of the mean value for decision making. Previous studies also lacked practicability in applications due to statistics from the asymmetrical data distribution. As a result, software developers could not effectively evaluate the possible risk related to the software release time. To improve the estimation, we employ the inflection S-shape model to propose the SRGM on the basis of confidence intervals assumed to come from the normal distribution. The proposed model allows determining the optimal time for software release with the consideration of its potential risk. For efficient determination, the architecture and user interface of the computation system are also proposed.

Effect of Extraction Parameters (Raw Material Particle Size, Volume of Solvent, and Time) on the Process Yield of Rice Bran Oil

Rice bran oil is the result of oil extraction from rice bran. Extraction solvent proccessing conducted using ethanol. Rice bran oil can be used as a consuming oil and materials for making cosmetics.This research aimed to find effect of rice bran size, ethanol volume, extraction time and its interaction toward yield of rice bran oil. This research include pure experimental study based on explorative factorial design to find the most influencing factors. The low level of rice bran size is 0.014 mm, beside the high level is 0.085 mm. The volume of ethanol that are used in this research, 250 ml as low level and 400 ml as high level. And then 120 minute as low level of extraction time whereas 240 minute as its high level. Data analysis of yield was done with Minitab software Release 16 with 95% level of confidence.The results show that the size of rice bran was dominant in determining the most influencing factors.

Software Bug Number Prediction Based on Complex Network Theory and Panel Data Model

Accurate software bug number prediction makes software test resource allocation, maintenance, and release time cost efficient. However, it is a challenge to accurately predict the number of software bugs when there fluctuations caused by many uncertain factors faced by the complex software. Considering this, a new method for software bug number prediction based on a panel data model from the perspective of complex networks is proposed in this article. Using complex network theory, we constructed the software code network and calculated the static metrics of the network structure, and the percolation threshold of change in the network structure based on percolation theory as a dynamic metric. These network metrics were then normalized as inputs and a panel data model was used for bug prediction. The proposed method can predict the number of bugs for both within-project and cross-project. Empirical studies were performed on data obtained from 120 releases of eight open-source software projects (Lua, SQLite, Redis, Linux kernel, ant, jmeter, poi, and tomcat), the experimental results indicated that network metrics are effective bug indicators, and the proposed method outperformed ten baseline methods (with an average improvement of 28.05%). This article is expected to provide insights into more smart software quality and reliability assurance.

A Case Study of Mobile Health Applications: The OWASP Risk of Insufficient Cryptography

Mobile devices are being deployed rapidly for both private and professional reasons. One area of that has been growing is in releasing healthcare applications into the mobile marketplaces for health management. These applications help individuals track their own biorhythms and contain sensitive information. This case study examines the source code of mobile applications released to GitHub for the Risk of Insufficient Cryptography in the Top Ten Mobile Open Web Application Security Project risks. We first develop and justify a mobile OWASP Cryptographic knowledgegraph for detecting security weaknesses specific to mobile applications which can be extended to other domains involving cryptography. We then analyze the source code of 203 open source healthcare mobile applications and report on their usage of cryptography in the applications. Our findings show that none of the open source healthcare applications correctly applied cryptography in all elements of their applications. As humans adopt healthcare applications for managing their health routines, it is essential that they consider the privacy and security risks they are accepting when sharing their data. Furthermore, many open source applications and developers have certain environmental parameters which do not mandate adherence to regulations. In addition to creating new free tools for security risk identifications during software development such as standalone or compiler-embedded, the article suggests awareness and training modules for developers prior to marketplace software release.

The Prevalence of Metabolic Syndrome, Scoring, and Comparison in People With and Without COPD: Evidence from Shahrekord PERSIAN Cohort Study.

Background: Comorbidities are common in patients with chronic obstructive pulmonary diseases (COPDs), including metabolic syndrome (MetS). This study aimed to determine the prevalence of MetS and its components in people with and without COPD. Methods: This population-based study was performed on 6961 adults in the Shahrekord Prospective Epidemiological Research Studies in Iran. Data collection, spirometry, and COPD diagnosis were performed according to the cohort protocol from 2015 to 2019. The data were analyzed by two independent sample t-tests, analysis of covariance, chi-square, and odds ratio (OR) with 95% confidence interval (95% CI) in logistic regression models. P value <0.05 was considered statistically significant. All analyses were conducted using Stata Statistical Software Release 16 (Stata Corp., College Station, TX). Results: The prevalence of MetS in patients with and without COPD was 28.4% and 31%, respectively. The most common components of MetS in people with COPD were low high-density lipoprotein cholesterol (47.4%), waist circumference (43.9%), and high fasting plasma glucose (39.3%). There was a statistically significant difference in the frequency of respiratory dysfunction between people with and without MetS. Being older than 60 (OR = 2.20, 95% CI: 1.72-2.80), female (OR = 1.36, 95% CI: 1.49-1.97), obese (OR = 11.17, 95% CI: 9.02-13.62), and uneducated (OR = 1.80, 95% CI: 1.49-2.17), and living in urban areas (OR = 1.96, 95% CI: 1.64-2.35) are stronger predictors of MetS in this population. Conclusion: There was no significant difference in the prevalence of MetS between patients with and without COPD. Spirometry parameters and respiratory problems were significant in subjects with and without MetS.

A software reliability growth model for imperfect debugging

In general, software reliability growth models (SRGMs) are often developed based on the assumptions of perfect debugging, single error type, and consistent testing environment. However, such the assumptions may be unrealistic for testing projects because new errors are always introduced during the debugging process, software errors are not alike, and the testing environment may change as the workforce escalates. Furthermore, the learning effect of the debugging process is taken under advisement, and assumes that it is unstable since the process of the error removal is also imperfect, which may cause a fluctuation of errors in the system. Therefore, the study is based on the Non-Homogeneous Poisson Process with considerations of the phenomenon of imperfect debugging, varieties of errors and change points during the testing period to extend the practicability of SRGMs. Furthermore, the error fluctuation rate is described by a sine cyclical function with a decreasing fluctuation breadth during the detection period, which indicates that the influence of increasing new errors during the testing period will be gradually unremarkable as the testing time elapses. Besides, the expected time of removing simple or complex errors is assumed to be different truncated exponential distributions. Finally, the optimal software release policies are proposed with considerations of the costs which occur in the testing and warranty period under an acceptable threshold of software reliability.

Promoting early management of frailty in the new normal: An updated software tool in addressing the need of virtual assessment of frailty at points of care.

IntroductionFrailty is a state of diminished physiological reserve and can be assessed using the frailty index. Early management of frailty is crucial for preventing adverse outcomes. Intended for assessing home‐living older adults, the initial release of the eFI‐CGA software was prior to the coronavirus disease 2019 (COVID‐19) pandemic.MethodsIn addressing the increased need of virtual assessment, the eFI‐CGA was upgraded to version 3.0. In this paper, we introduce the updated electronic frailty assessment tool, reporting the newly developed features and validating its use.ResultsEnd‐user experiences with the previous versions are discussed. The updated features include a search function to resume disrupted assessments. The improved user interface enabled clinicians to record care management details.ConclusionThis study represents an example of software solutions in moving from disruption to transformation, benefiting healthcare for older adults during this challenging time.

Software reliability and cost models with warranty and life cycle

With increasing number of tech-savvy users, the demand for software with advanced features is also growing exponentially. To meet these expectations, software firms are coming up with innovative strategies to attract customers to buy their products and remain loyal. One of the key strategies is to provide product assurance is post-sales support. Software firms provide a warranty to fix the failures encountered during the operational phase for a pre-defined time period. Software reliability literature consists of various modeling framework to decide release time under pre-defined warranty period under the consideration of no post-release testing. Very limited work has been done in the direction of post-release testing with warranty. In this work, we propose a framework to obtain optimal release and warranty time of software to minimize the overall cost under reliability constraint. We have provided the algorithm to obtain the analytical solution of release and warranty time. To make our proposed work more realistic we have considered the change point phenomenon and imperfect debugging of fault in our modeling framework. We have also conducted a sensitivity analysis on the model parameters to check their impact of the firms scheduling strategy. The proposed work is validated on a real-life data set.

Structural abnormalities in paediatric moyamoya disease revealed by clinical magnetic resonance imaging, regionally distributed relative signal intensities and volumes.

Moyamoya disease (MMD) is a rare, progressive cerebrovascular disorder, with an unknown aetiology and pathogenesis. It is characterized by steno-occlusive changes at the terminal portion of the internal carotid artery (ICA), which is accompanied by variable development of the basal collaterals called moyamoya vessels. In this study, we investigate the potential for structural T1 magnetic resonance imaging (MRI) to help characterize MMD clinically, with the help of regionally distributed relative signal intensities (RRSIs) and volumes (RRVs). These RRSIs and RRVs provide the ability to characterize aspects of regional brain development and represent an extension to existing automated biomarker extraction technologies. This study included 269 MRI examinations from MMD patients and 993 MRI examinations from neurotypical controls, with regional biomarkers compared between groups with the area under the receiver operating characteristic curve (AUC). Results demonstrate abnormal presentation of RRSIs and RRVs in the insula (15- to 20-year old cohort, left AUC: 0.74, right AUC: 0.71) and the lateral orbitofrontal region (5- to 10-year old cohort, left AUC: 0.67; 15-20 year cohort, left AUC: 0.62, right AUC: 0.65). Results indicate that RRSIs and RRVs may help in characterizing brain development, assist in the assessment of the presentation of the brains of children with MMD and help overcome standardization challenges in multiprotocol clinical MRI. Further investigation of the potential for RRSIs and RRVs in clinical imaging is warranted and supported through the release of open-source software.

OurRank: A Software Requirements Prioritization Method Based on Qualitative Assessment and Cost-Benefit Prediction

Requirements prioritization is an activity aimed at determining the essential requirements to include in a software release. Although there are several prioritization methods to systematize this task, there are still unresolved challenges. Existing methods do not guarantee that requirements prioritization meets stakeholder expectations and goals. This is because most prioritization methods operate by considering only quantitative information, making it difficult to formally capture stakeholder interests and perspectives that can rather be made explicit in qualitative terms. Likewise, methods including qualitative information only consider elements associated with benefit estimation, that is, positive aspects of the project, but neglect costs or negative aspects. As a result, the prioritization process is driven by a partial view of constraints. Such methods also fail at capturing and combining expert knowledge that decision-makers can bring into the decision-making process. In this research, we propose a novel method for software requirements prioritization, which facilitates incorporating experts’ qualitative assessment at the outset of the prioritization process and considers both benefit and cost constraints. Details of the method are presented, together with a case study describing a real application scenario. Recommendations and guidelines regarding the application of the method are proposed based on the results of the case study.

Application Research on Key Points of Software Security Development Technology

With the rapid development of network technology, software security has attracted more and more attention. Based on the software security lifecycle presents an improved suitable for small and medium-sized enterprise software security development process, make the software safety to cover the whole software life cycle, improve the level of safety of encoding software. The model used in the software development phase, test phase and release phase three software security key technologies are studied and discussed in detail. In addition, it also designed a software security development management platform, the software development process so that the model can better apply to the actual enterprise to the platform. After a number of units for a period of time of trial, get a better evaluation of the households.

Optimisation of software release time using adaptive neuro fuzzy approach

Cost and reliability are two important factors that govern the release time of software. These parameters are estimated with respect to mean number of failures in the software. A software release time growth model is used to determine the reliability of the software. The most important decision taken by the software development team is to release a software with maximum reliability and minimum cost. Researchers have discussed various optimisation methods to find the optimal release time. In this research study, an adaptive neuro fuzzy optimisation method is discussed with different input criteria to find the optimal time of release of software. Different input criteria considered in this research are cost and reliability of the software. Adaptive neuro fuzzy optimisation method has the advantage of fuzzy logic as well as neural network. Numerical examples are explained and results are compared with the other optimisation methods. From the results of the numerical example, it can be concluded that the neuro fuzzy optimisation method is best for software release time optimisation.

Optimisation of software release time using adaptive neuro fuzzy approach

Optimisation of software release time using adaptive neuro fuzzy approach

A hybrid multi-objective optimization algorithm for software requirement problem

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.

New tools for automated cryo-EM single-particle analysis in RELION-4.0.

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a variable-metric gradient descent algorithm with adaptive moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on 12 publicly available cryo-EM data sets.

User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.