Software Reliability Research Articles

Influencing Factors' Analysis for the Performance of Parallel Evolutionary Test Case Generation for Web Applications

ABSTRACTEvolutionary test case generation plays a vital role in ensuring software quality and reliability. Since Web applications involve a large number of interactions between client and server, the dynamic evolutionary test case generation is very time‐consuming, which makes it difficult to apply in actual projects. Obviously, parallelization provides a feasible way to improve the efficiency and effectiveness of evolutionary test generation. In our previous research, the idea of parallelism has been introduced into the evolutionary test generation for Web applications. However, its performance is affected by many factors, such as migration scale, migration frequency, the number of browser processes and subpopulations, and so on. The analysis of influencing factors can guide enhancing the performance of evolutionary test generation. For this reason, this paper analyzes the factors that influence parallel evolutionary algorithms and how they affect the performance of test generation for Web applications. At the same time, different parallel evolutionary test generation methods are designed and implemented. Experiments are conducted on open‐source Web applications to generate test cases that meet the server‐side sensitive paths coverage criterion, providing guidance and suggestions for the parameter setting of parallel evolutionary test case generation for Web applications. The experimental results show that (1) compared with the global parallelization model, the evolutionary algorithm based on the parallel island model has a greater improvement in test case generation performance. In more detail, when generating test cases with the same server‐side sensitive paths coverage, the number of iterations required is reduced by 49.6%, and the time cost is reduced by 58.7%; (2) for the test case generation based on the parallel island model, if the migration scale is large, appropriately increasing the migration frequency can reduce its time cost; (3) if the number of subpopulations is fixed, appropriately increasing the number of browser processes can reduce the time cost of Web application test case evolution, but the number of browser processes should not be too large; otherwise, it may increase the time cost.

An In-Depth Approach to Strengthening Security in Open-Access Libraries Utilizing JSON Web Tokens (JWT)

In response to growing security concerns in software development, this study introduces an open-access library designed to enhance authentication systems using JSON Web Tokens (JWT). This research addresses critical challenges in integrating secure authentication mechanisms by developing a new, scalable, user-friendly library focused on security and ease of implementation. The library incorporates JWT rotation, Redis integration, and customizable validation to ensure robust, adaptable security for developers. Utilizing an agile, Extreme Programming (XP) methodology, the library was iteratively tested and optimized based on real-world developer feedback. The result of the new library shows improved usability, flexibility, and token management efficiency, demonstrating the effectiveness in supporting secure authentication practices compared to the state-of-the-art libraries. The new library is offering a practical, open-source solution to strengthen authentication systems in modern web applications, advancing the accessibility of secure, reliable software development tools.

Multi-level risk classification of distributed embedded software failures for autonomous systems

With increasing autonomy in systems, the role of software becomes more prominent as it overtakes human operator functions. The software in autonomy differs from automation with respect to functionality, implementation, and complexity, and software failures contribute to system and operational risk. Such failures, however, are often not sufficiently catered for in current risk assessments and mitigation processes, as they are challenging to identify and quantify, in particular, in the early conceptual design phase. Software reliability is not the same as software safety, as the latter encompasses the context and use of the software, as well as interactions and potential cascading failures to hardware, humans, and the environment. It is also difficult to investigate cascading effects on the system that may follow from software failures. The objective of this paper is to propose a novel classification taxonomy to support a more thorough identification of software failures for systems with different degrees of autonomy, as well as for software implementation techniques. The risk from software is interwoven into the design, development, validation, and verification processes, impacting safe operation. The proposed taxonomy can be used iteratively from the early design phase as the detailed design concepts evolve. The level of abstraction for system and software functions decreases with the design and development process. The validation and verification processes must ensure the software’s safety and reliability on different system abstraction levels. The software taxonomy in this paper includes relevant causes, consequences, and process relationships, and has been created based on existing industry classifications, research, and system models. A case study applying the taxonomy to navigation and collision avoidance functions on the subsystem level of a Maritime Autonomous Surface Ship (MASS) is performed. Software properties extracted from existing systems and knowledge are transformed into a functional model. Each software failure is then described in the context of the system level valid for the design, development, validation, and verification processes for MASS. The overall outcome of the paper may contribute to the safer design of systems through enhanced identification of potential hazards and software failures, leading to improved risk assessments and, as such, a better basis for defining more efficient safety requirements for autonomous systems from the early system development. Even though the paper exemplifies the taxonomy and classification by focusing on MASS, the work has relevance to other types of software-intensive systems.

Estimating Parameters of Software Reliability Growth Models Using Artificial Neural Networks Optimized by the Artificial Bee Colony Algorithm Based on a Novel NHPP

Estimating Parameters of Software Reliability Growth Models Using Artificial Neural Networks Optimized by the Artificial Bee Colony Algorithm Based on a Novel NHPP

Early Phase Software Dependability Analysis: A Neutrosophic Inference System-Based Approach

Due to the presence of reliability, security, or performance-related issues, software systems will become nondependable during the early development phase. Currently, there is a lack of research addressing these dependability issues in software quality analysis. To bridge this gap, this study proposes a neutrosophic inference system (NIS)-based model to predict reliability, security and performance attributes during the early phase. The NIS model accommodates uncertainties, imprecisions, indeterminacies and incompleteness of metric values utilizing its truth, indeterminate and false components. To enhance the prediction accuracy of the NIS model, a rule-base formation algorithm is proposed for NIS considering domain expert knowledge. Finally, an artificial neural network (ANN) model is designed based on estimated values of reliability, security and performance attributes to predict the total number of faults in software projects. Comparative analysis demonstrates that the proposed model outperforms other existing models. This proposed methodology helps software developers in assessing software dependability from the beginning stage of software development.

Enhancing Site Reliability Engineering (SRE) Observability: A Comprehensive Approach

The subject of applying software engineering techniques to IT operations to create scalable and highly reliable software systems is known as site reliability engineering, or SRE for short. To preserve system’s health, it heavily emphasizes automation, ongoing improvement, and efficient incident response. Observability, is the tracking and examination of system outputs in the form of metrics, logs, and traces with the goal of providing insight into the system's internal state, is one of the fundamental principles of SRE. This keeps systems reliable and effective by enabling the early detection and resolution of problems. SRE and observability work together to help businesses achieve high IT operations performance and reliability. The contemporary IT environment is becoming more and more complex as industries growing infrastructure use on-premises and cloud hosting, adding microservice design along with traditional monolithic application architectures. In the pursuit of observability, this diversity presents unique difficulties for SRE. This article sheds light on the significance of SRE observability, tools and tactics used by enterprises, and how site reliability engineering could be improved across numerous industries with the use of an open-source framework like Open Telemetry to strengthen observability.

Prototype of 3D Reliability Assessment Tool Based on the Deep Learning Considering OSS Effort Wiener Process Data

At present, the fault big data of open source software are opened as the open data set. In particular, the fault detection phenomenon depends on various situation of operation in OSS. Actually, various software reliability growth models have been actively proposed by several researchers in the past. This paper applies the deep learning approach to the OSS fault big data. Then, we propose several reliability assessment measures based on the deep learning. As an approach, the range of estimate expands by the Wiener process embedded for the data preprocessing. Furthermore, this paper proposes the performability as novel reliability assessment measure from the proposed deep learning model. In particular, we develop the prototype of 3D reliability assessment tool. Several illustration examples based on the developed prototype of 3D reliability assessment tool by using the actual fault big data sets are shown in this paper.

FlowCron - Increasing access to HPC by wrapping Globus into a function-as-a-service

Despite significant investment in High-Performance Computing (HPC) clusters by funding councils, there are still many researchers whose workflows could not benefit from the computation speed that is provided by these clusters. Reducing barriers to entry for these researchers would accelerate their scientific throughput, since they will be able to respond to results in a timely fashion, improving either their protocols or correcting any problems that might have arisen. This improves the quality of science, and therefore the return on investment, in computationally-intensive areas such as Cryogenic Electron Microscopy (cryo-EM). This paper outlines a technique, FlowCron, for users to analyse their data on a HPC facility with minimal training, increasing accessibility. FlowCron transfers the responsibilities of installation and upkeep of data processing pipelines from users to HPC systems administrators, simplifies the set up of HPC pipelines, and makes pipelines as reliable as possible once set up. The work described here has software dependencies that are common to the majority of HPC clusters. We achieve this by linking Globus and cron to produce an open-source system that requires little administrative support but provides a very easy way of running an analysis on a HPC system. The user starts the analysis through the Globus website and, when started, the data will be encrypted, uploaded to the HPC, analysed, and returned to the originating machine, along with a record of the analysis.

Unveiling the Evolution: Multi-Patch Multi-Release Software Reliability Growth Model with Testing Effort

Reliance on software has increased expectations for software organizations to deliver high-quality software to meet the increasing demand from end-users. Continuous testing is imperative to ensure software quality, yet prolonged testing can lead to increased market opportunity costs. Consequently, organizations often opt to release software early and subsequently conduct testing during the operational phase, addressing existing bugs through patch deployment. These patches, small programs aimed at fixing, improving, or updating software, serve to rectify security vulnerabilities or bugs efficiently. For minor changes, patch releases prove more practical and cost-effective than launching entirely new software versions. The adoption of multi-release software endows developers with a competitive advantage, catering to the diverse needs of end-users. This paper introduces a testing effort-based software reliability growth model, evaluating the impact of multi-patching on multi-release software. The model operates under the assumption of continuous fault removal post-release, using different distribution functions to construct three framework variations. Parameter estimation employs the Statistical Package for Social Sciences, with a real dataset serving as a basis for a numerical example illustrating the model’s practical application. Additionally, a comparative analysis of model performance, based on different distribution functions, is conducted through multi-criteria decision-makingtechniques.

Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm – Evolutionary Algorithm

Background: With the growing advances in the digital world, software development demands are increasing at an exponential rate. To ensure reliability of the software, high-performance tools for bug report analysis are needed. Aim: This paper proposes a new ‘Iterative Software Reliability’ model based on one of the most recent Software Development Life Cycle (SDLC) approach. Method: The proposed iterative failure rate model assumes that new functionality enhancement occurs in each iteration of software development and accordingly design modification is made at each stage of software development. In terms of defects, testing effort, and added functionality, these changing needs in each iteration are reflected in the proposed model using iterative factors. The proposed model has been tested on twelve Eclipse and six JDT software failure datasets. Proposed model parameters have been estimated using a hybrid swarm - evolutionary algorithm. Results: The proposed model has about 32% and 55% improved efficiency on Eclipse and JDT datasets respectively as compared to other models like Jelinski Moranda Model, Shick-Wolverton Model, Goel Okumotto Imperfect Model etc. Conclusion: In each analysis done, the proposed model is found to be reaching acceptable performance and could be applied on other software failure datasets for further validation.

Critical Contingency Analysis: Enhancing Stability in Nigeria's 330kv Grid

This study presents a contingency ranking analysis for the Nigerian 330kV power system using ETAP software. The network model, consisting of 65 buses, 120 transmission lines, 16 generators, and 14 loads, was evaluated to identify critical contingencies affecting system performance. Results indicate that the outage of the M2S line poses the most significant threat to grid stability, while the SAPELE NIPP generator failure has negligible impact. Recommendations focus on enhancing system resilience through load management, network restructuring, and renewable energy integration. Limitations of the study include model simplification and software dependency, suggesting areas for future research expansion

Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies.

The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outcomes. Surrogate modelling has shown promise, but challenges remain, particularly in cardiovascular applications, due to the complexity of parametrising anatomical structures and the need for large training datasets. This study aims to apply statistical shape modelling and machine learning for predicting stent deployment in real time using patient-specific models. ► METHODS:: We built a statistical shape model starting from an open-source clinical dataset, which we then used to generate new synthetic cases. Finite element simulations of stent deployment were performed on these cases using an in-house software. A surrogate model was then trained to map the statistical features of the synthetic models to the corresponding stent configurations, evaluating sensitivity to dataset size. ► RESULTS:: Even with the smallest dataset (400 samples), the average prediction error in position among the tested cases never exceeded 8.6%, with a median one within the testing dataset of 1.6%. As the number of training samples increased (4900), we achieved a median position error lower than 0.1 mm (0.97%) and a maximum position error of 0.5 mm (4.8%). Notably, the largest errors occur in the radial direction of the stent, while the deployed length is accurately predicted in all the cases. ► CONCLUSIONS:: The consistent success in performance strongly suggests that surrogate modelling represents a clinically valuable tool for accurately computing stent deployment outcomes in real time, even within complex anatomical scenarios.

Radar software reliability test case intelligent generation based on domain knowledges

Radar software reliability test case intelligent generation based on domain knowledges

The Role of Test-driven Development in Enhancing Software Reliability and Maintainability

The Role of Test-driven Development in Enhancing Software Reliability and Maintainability

Yet another least squares estimation in NHPP-based software reliability models with grouped data

Yet another least squares estimation in NHPP-based software reliability models with grouped data

Integrating Human Learning Factors and Bayesian Analysis into Software Reliability Growth Models for Optimal Release Strategies

Integrating Human Learning Factors and Bayesian Analysis into Software Reliability Growth Models for Optimal Release Strategies

Laser Fault Injection Methodology for Software Reliability Testing

Laser Fault Injection Methodology for Software Reliability Testing

Impact in Software Testing Using Artificial Intelligence: A Literature Review

Software (SW) testing is more effective in software development cycle life, with increase in software complexity and society’s growing reliance on software across various sectors. Software testing is a critical step; however, it often requires substantial time and financial resources, making it challenging to achieve comprehensive coverage. Artificial intelligence (AI), particularly through machine learning and reinforcement learning techniques, offers trans-formative solutions to these challenges, especially in white-box testing. This paper focuses on leveraging AI techniques - specifically Ensemble Learning and Swarm Intelligence algorithms to optimize software testing. Swarm Intelligence, which imitates the collective behavior of natural organisms, is effective in identifying efficient paths within the source code. This paper includes review various methods such as bee algorithms, ant colony optimization, and particle swarm optimization, all of which enhance error detection speed and accuracy while minimizing resource consumption. When combined with Ensemble Learning, which aggregates results from multiple models, these AI techniques foster robust decision-making and comprehensive test coverage. This integrated approach not only addresses issues related to control and data flow but also significantly contributes to achieving a more efficient and reliable software testing process, ultimately reducing both the time and costs associated with testing.

A Systematic Literature Review on Characteristics Influencing Software Reliability

Reliability, as a non-functional requirement, is a crucial aspect that refers to the system's ability to perform its intended functions consistently and without failure over an extended period. It is essential in designing and implementing software systems, as it affects software quality. Maintaining software reliability is a significant challenge, as it is directly impacted by factors such as the complexity of the software design, the amount of code, and the measures taken to secure the system from unauthorized use. There are significant growing appeals for predicting reliability to account for risks. Research on reliability risk assessment has a long tradition; unfortunately, comprehensible reliability characteristics are still vague when determining potential risks. Clearly defining, prioritizing, and addressing reliability characteristics is essential for delivering reliable, high-quality software that meets user needs and business goals. The ignorance and lack of comprehensive reliability characteristics have evolved into inaccurate risk assessment, triggering malfunctions in the operational environment. Comprehensive characteristics are key elements to predict and estimate software reliability. The reliability characteristics could determine the precise objective of reliability efforts. This systematic literature review aims to identify the key characteristics influencing software reliability, the potential risks associated with these characteristics, and the metrics used to measure and assess them. Thirty-one research articles related to research questions have been reviewed. The findings indicate that comprehensive reliability characteristics could identify, classify, and prioritize potential risks, improving current metrics. It can be concluded that the accurate potential reliability risk can demonstrate the consequence of failure.

Qualified Digital Ecosystems As an Academic Information System Framework in The Industrial 4.0

Leveraging quality models to establish a robust digital ecosystem is imperative in the Industry 4.0 era. Existing Academic Information System (AIS) software often lacks comprehensive adoption of quality models as core system instruments. As a critical component of the education sector's digital transformation, AIS represents a vital ecosystem for facilitating efficiency and innovation in this industrial era. This study addresses these gaps by implementing a comprehensive quality framework for AIS, integrating cloud computing for enhanced security and privacy, and employing the Web-Based Application Quality Model (WBAQM) as the primary development framework. The research also utilizes ISO 9126 and ISO/IEC 25010 quality models to establish usability and security standards, while the Academic Information System Quality Instrument (AISQI) is introduced to evaluate system performance. Additionally, the COBIT framework is applied to audit and enhance AIS functionality, ensuring alignment with academic and operational needs. The findings demonstrate that adopting quality models significantly improves the reliability, usability, and security of AIS software, delivering a system that aligns with both functional requirements and user expectations. This research contributes to the digital transformation of educational institutions in Indonesia by providing a scalable and efficient AIS framework. By aligning with Industry 4.0 principles, the proposed model ensures quality assurance and fosters innovation, enabling educational institutions to adapt to evolving technological demands and improve overall institutional performance.