Software Release Research Articles

Enhancing Environmental Sensitivity and Vulnerability Assessments for Oil Spill Responses in the Caspian Sea

Oil spills pose significant threats to marine and coastal ecosystems, necessitating advanced methodologies for environmental sensitivity and vulnerability assessments. This study enhances existing frameworks to better manage oil spill risks in the Caspian Sea, a region characterized by its ecological sensitivity and economic dependence on oil extraction. Utilizing the Environmental Sensitivity Index (ESI), we adapted global standards to the unique conditions of the Caspian Sea and built a sensitivity map of the coastline, which later became one of the components of the integral sensitivity map for the entire Caspian Sea, which includes several biotic and abiotic components. We also developed a comprehensive geodatabase incorporating topographic, infrastructural, and hydrodynamic data. Through the sophisticated modeling of oil spill scenarios using the Oil Spill model of the MIKE 21 software (Release 2016) suite, we simulated spills of varying magnitudes to analyze their potential impacts on the marine and coastal environment. The results enabled the creation of vulnerability maps, pinpointing areas at highest risk and facilitating strategic response planning. Our study demonstrates the critical importance of integrating advanced geospatial analyses and dynamic modeling techniques to improve oil spill preparedness and response strategies. The findings of this study suggest that enhanced monitoring and adaptive management strategies are essential for protecting the Caspian Sea from environmental risks posed by its oil industry.

PO0210: Evaluating the Pre-Clinical Release of a Commercial Auto-Contouring Software for Use in CT-Based Gynecological Brachytherapy

PO0210: Evaluating the Pre-Clinical Release of a Commercial Auto-Contouring Software for Use in CT-Based Gynecological Brachytherapy

KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction

Understanding and predicting the bug type is crucial for developers striving to enhance testing efficiency and reduce software release problems. Bug reports, although semi-structured, contain valuable semantic information, making their comprehension critical for accurate bug prediction. Recent advances in large language models (LLMs), especially generative LLMs, have demonstrated their power in natural language processing. Many studies have utilized these models to understand various forms of textual data. However, the capability of LLMs to fully understand bug reports remains uncertain. To tackle this challenge, we propose KnowBug, a framework designed to augment LLMs with knowledge from bug reports to improve their ability to predict bug types. In this framework, we utilize bug reports from open-source deep learning frameworks, design specialized prompts, and fine-tune LLMs to assess KnowBug’s proficiency in understanding bug reports and predicting different bug types.

Testing coverage based NHPP software reliability growth modeling with testing effort and change-point

This paper investigates a software reliability growth model based on non-homogeneous Poisson process that includes coverage testing and change-points. The model integrate effort spent into a model to evaluate software reliability based on testing coverage. The amount of testing effort spent follows the Weibull distribution, while the amount of coverage is modeled by logistic, delayed S-shaped and exponential functions. Additionally, we look into the cost requirement-based software release time for exponential functions with a reliability constraint. We introduce the genetic algorithm, which is a powerful tool for dealing with search and optimization issues. For the purpose of testing the model's goodness of fit, two real failure datasets are used, and the model’s performance is analyzed based on goodness-of-fit metrics. By comparing the proposed model to perfect debugging models that have been described in the literature, it is demonstrated that the proposed model demonstrates notable advancements in this regard. The results of our analysis indicate that a software reliability estimation model should include change-points in a testing process if they exist.

Shift-left approach for Vulnerability Management in SDLC

Security vulnerabilities in software development can lead to severe risks if not addressed promptly. By adopting a shift-left approach and implementing gating mechanisms in Continuous Integration/Continuous Delivery (CI/CD) pipelines, organizations can mitigate the impact of high and critical vulnerabilities early in the Software Development Life Cycle (SDLC). This paper discusses a practical methodology for integrating security gates into the CI/CD pipeline to prevent the release of software with critical security flaws. Keywords —Vulnerability Management, Shift-Left Security, CI/CD Pipeline, High Vulnerabilities, Critical Vulnerabilities, Security Gating, SDLC, GitLab, Automation.

A multi-step mathematical model-based predictive strategy for software release timing during testing stage

Mathematically precise modeling is important to be established to accurately examine the quantitative relationship between software testing and software reliability. Software testing process is complex since it is concerned with various factors such as test case execution, defect debugging, tester expertise, test case selection, and so forth. For this reason, it is required to be meticulous in formulating the software testing process in a manner which is mathematically concise. The software release life cycle or sequential release timeline, referring to the process related to the development, testing and distribution of a software product comprises several critical stages, and the length of this particular life cycle reveals variations depending on different factors like the type of product, the intended use of it, industry security, general standards and compliance. One consideration software engineers have is related to the release date of the software so that future commitments about the software’s release time can be formulated beforehand. In view of these aspects, a multi-step strategy for predicting software release dates is proposed in the current study along with the following stages: firstly, the proposed technique selects the utmost reliability growth model that very well fits the observed test data halfway through the testing period, and then employs it to forecast the probable date of release. This technique entails approximating the unknown parameters of suitable Software Reliability Growth Models (SRGMs). Finally, the chosen SRGM is used to forecast the release date of the software under test by fitting it to available fault data. The proposed method is straightforward and applied to test on a total of ten actual datasets collected from the literature. The results of the proposed technique reveal that in the majority of the situations, nearly exact approximation of date of release can be made halfway through the testing period. Moreover, the proposed method’s performance is also compared to that of a number of previous strategies present in the literature. The outcomes obtained by our study demonstrate that the proposed strategy may be used to forecast the release date of software in practical situations.

ПОСТРОЕНИЕ МОДЕЛИ РАСЧЕТА ВРЕМЕНИ ВЫПУСКА ДОРАБОТОК ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ ПОСЛЕ ВНЕДРЕНИЯ СИСТЕМЫ УПРАВЛЕНИЯ РЕЛИЗНЫМ ЦИКЛОМ

The paper proposes a mathematical model for calculating the release time of a new software release. This model allows you to evaluate the difference between the operation of a production system during the release cycle and after it. The relevance of the proposed methodology determines the need for its application to the problem of assessing the effectiveness of an information complex and an automated release process cycle. The object of the study is the information department of the bank, ensuring automation – the process of estimating the release time of software improvements. The goal of the work is to build a model for calculating the release time of software improvements after the implementation of a release cycle management system, ensuring regular, fast and stable implementation of new software versions. To achieve this goal, within the framework of the work, an architectural solution for a system that automates the release cycle process was designed; a new process of personnel interaction was modeled after the implementation of the release cycle system; a pipeline for automatic delivery of software to an industrial environment and a process for automatic packaging of software into containers have been developed; a mathematical model was built to calculate the release time of software improvements after the release cycle management system. To solve these problems, we used the Gitlab CI/CD class system, which provides the ability to automatically assemble containers and deploy them into environments, and the Deckhouse system for container orchestration.

Modeling the impact of noise and uncertain operational environment on software release decisions considering testing coverage-based SRGM

The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.

Updated Review of Longshore Sediment Transport: Advantages, Disadvantages, and Comparisons Using a Satellite Image Methodology

This review updates the different categories and formulations of the calculation of longshore sediment transport (LST) and summarizes their advantages and disadvantages. Most of these methodologies require calibration for areas different from those studied by their authors. Thus, a method of validation and calibration is presented here by processing satellite images with CoastSat software (release v 2.7) to determine accretion and erosion volumes. This low-cost methodology was applied to Salaverry Beach (Peru) to compare the results of the different formulations. A range of variation between −96% and +68% was observed concerning the error, with van Rijn’s formula being the most accurate for this particular case.

Evaluating and Incorporating Generative AI in Nursing Informatics and Data Science Graduate Courses.

The disruptive effect and lasting implications of generative artificial intelligence ("Gen AI") tools for higher education are yet unknown. ChatGPT 3.5 was released in November 2022 and soon had over 100 million users. This public software release triggered a global Gen AI race among technology industry giants. Educators in higher education were unprepared for this seismic shift. For the Fall 2023 term, we revised two graduate courses in nursing informatics and data science using generative AI to anticipate broad student awareness of these tools. The aim of this report is to present two different examples of course component evaluation using publicly available Gen AI tools to inform course component revisions. This effort was undertaken to "proof" assignments against inappropriate use of Gen AI, revise assignments to incorporate use of Gen AI tools, and inform course policies about appropriate use of Gen AI for course work.

SERP4IoT'24 Workshop Report

We face a new software crisis. In 1968, computer scientists learned that developing robust software requires skills, methods, and tools. Today, software and hardware engineers realize that developing a robust Internet of Things (IoT) also pushes the states of their art and practice. Recent news illustrates the many problems IoT faces: from lack of interoperability to broken updates to massive security attacks. In this context, the 6th International Workshop on Software Engineering Research and Practices for the Internet of Things (SERP4IoT) aims to provide a highly interactive forum for researchers and practitioners to address the challenges of, find solutions for, and share experiences with the development, release, and testing of robust software for IoT systems.

SGT: Aging-related bug prediction via semantic feature learning based on graph-transformer

Software aging, characterized by an increasing failure rate or performance decline in long-running software systems, poses significant risks including financial losses and potential harm to human life. This is primarily attributed to the accumulation of runtime errors, commonly referred to as aging-related bugs (ARBs). ARBP aims to detect and address ARBs before software release, optimizing testing resource allocation. However, ARBP’s effectiveness relies heavily on dataset quality. Prior research often relied on manually designed metrics that lack semantic features, resulting in low prediction accuracy. Some studies construct models to learn semantic features from source code, but typically focus on token-level features from abstract Syntax Trees, neglecting critical topological and functional connections. In this paper, we introduce the SGT model, an ARBP method based on Graph-Transformer. This model efficiently extracts semantic information and logical structures from source code, capturing data dependencies and program dependencies. We also propose sub-graph sampling based on node degree to reduce structural complexity and apply random oversampling to address class imbalance. Experiments on three projects demonstrate notable improvements. For instance, SGT achieved F1 scores of 0.726 and 0.706 on Linux and MySQL, respectively. Compared to ALW, SGT shows a 12.3% and 6.8% improvement on Linux and MySQL.

Modeling Reliability-Driven Software Release Strategy Considering Testing Effort with Fault Detection and Correction Processes: A Control Theoretic Approach

In this paper, we have introduced a software reliability growth model (SRGM) that integrates a Weibull testing effort function (TEF) within the software fault detection process (FDP) and fault correction process (FCP), effectively capturing the intricacies of software testing. Leveraging the least squared estimation method, we estimated both model parameters and the TEF, leading to a notably improved fit with the dataset and enhancing our comprehension of software reliability dynamics. Our approach also involved employing a reliability-based method to identify the optimal time for software release, further reinforcing the robustness of our model. Subsequently, we devised an optimal control model to minimize testing efforts throughout the software development life cycle. This work provides a comprehensive methodological framework for computing the total cost while ensuring debugging costs follow a learning curve pattern. Through numerical simulations involving hypothetical values and the Weibull TEF, we observed that once the software achieves the desired reliability for release, subsequent costs gradually decline to zero. In essence, both testing and future costs converge to zero post-release, reducing instantaneous expenses.

Assessing MRI referrals’ appropriateness for low back pain post a radiology-initiated intervention

IntroductionThis study evaluated a pilot intervention to reduce low-value Magnetic Resonance Imaging (MRI) referrals for Low Back Pain (LBP). MethodsThis before-after intervention study analysed MRI referrals for LBP at two private imaging centres in Norway. MRI referrals for LBP obtained before and after an intervention of information campaigns and sending a return letter to clinicians for declined referrals were evaluated on information, quality, and justification rates. Four radiologists and two radiographers assessed the referrals. A point system was used to calculate referral quality. Each referral was given a score ‘good’ when rated above 5.5 and ‘poor’ below 2.5. Justification was based on assessors categorised rating as justified, unjustified or need more information. Stata Statistical Software (Release 18) was used for analysis. A mixed model analysed variations of the referrals pre- and post-intervention. A p-value of <.05 in variations was considered statistically significant. ResultsA total n = 300 patients’ referrals (150 referrals pre- and post-intervention) were collected and assessed. Post-intervention, 68% of referrals were justified, up from 63% pre-intervention. The assessment showed a 4% decrease in referrals with poor scores and a 2% increase in those rated as good or intermediate quality post-intervention. These changes were not statistically significant. ConclusionIt is important to state that it was not possible in our study to identify the subgroup of referrals that are known to be from clinicians who had received a return letter, although the information campaign targeted all referrers. Despite the limitations our findings suggest that providing reasons for declined referrals can serve as an educational tool for clinicians and contribute to the reduction of low value MRI for LBP. Implications for practiceRadiology department initiatives that raise awareness and offer referral criteria guidance to clinicians can serve as valuable educational tools, and further emphasize the importance of providing comprehensive information in MRI referrals for LBP.

Optimizing software release decisions: a TFN-based uncertainty modeling approach

Optimizing software release decisions: a TFN-based uncertainty modeling approach

A comprehensive analysis of challenges and strategies for software release notes on GitHub

A comprehensive analysis of challenges and strategies for software release notes on GitHub

Modeling Software Release Time and Software Patch Release Time Based on Testing Effort and Warranty

In this world of software technology, our dependency on software’s is increasing continuously. As a result, software industries are working hard to develop highly reliable software and to meet the expectation of customers. Generally, software companies release software early in market to take gain market share, but rigorous software testing is required for early release software to ensure reliability of software and meet the customer’s expectations. This requires a huge amount of resources, and it increases financial burden on the company, consequently, decreases the overall profit of company. Further, late release due to prolong testing of a software may improves reliability but results into a loss of market opportunity cost or may not be fulfil the customer’s aspirations. As a result, to stay competitive, companies release software early and release patches later to fix the bugs, improve the functionality of software, and to update the software. Software industries are improving the performance or usability of software by releasing patches which may increase the consumption of testing effort and consequently increase in cost. On the other hand, software firms also provide warranty on their products. To address the above said issues, we have developed a testing effort-based software reliability growth model, which incorporates warranty policy and estimates the optimal software release and patch time with the objective to minimise the total testing cost. Further, we have used Genetic Algorithm (GA) to estimate optimum software release and patch time. A numerical illustration has been presented on a real time data set to validate the proposed model.

Empowering software reliability: Leveraging efficient fault detection and removal efficiency

Advancements in science and technology have led to the widespread use of computer systems in various applications, emphasizing the importance of software reliability. Software failures can have severe consequences, making thorough testing crucial. Software reliability growth models (SRGMs) play a significant role in enhancing reliability by predicting improvement over time. This article introduces a comprehensive approach to software reliability that incorporates a dynamic fault detection rate, along with fault removal efficiency. The fault detection rate measures the rate at which faults are identified during testing, reflecting the effectiveness of the testing process. By incorporating this dynamic component, the model provides a more accurate estimation of software reliability and enables adaptive testing strategies and resource allocation. Achieving a high fault detection rate is desirable, but organizations must consider the cost implications and strike a balance between reliability and time-to-market constraints. This article extends the analysis to calculate the optimal release time and optimal warranty period that minimize development costs, subject to the desired reliability. By considering these factors, development teams can make informed decisions regarding the timing of software release and the duration of the warranty period, optimizing both reliability and cost.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Estimation of distribution algorithms with solution subset selection for the next release problem

Abstract The Next Release Problem (NRP) is a combinatorial optimization problem that aims to find a subset of software requirements to be delivered in the next software release, which maximize the satisfaction of a list of clients and minimize the effort required by developers to implement them. Previous studies have applied various metaheuristics, mostly genetic algorithms. Estimation of Distribution Algorithms (EDA), based on probabilistic modelling, have been proved to obtain good results in problems where genetic algorithms struggle. In this paper we propose to adapt three EDAs to tackle the multi-objective NRP in a fast and effective way. Results show that EDAs can be applicable to solve the NRP with rather good quality of solutions. Furthermore, we prove that their execution time can be significantly reduced using a per-iteration solution subset selection method while maintaining the overall quality of the solutions obtained, and they perform the best when limiting the search time as in an interactive tool that requires fast responsiveness. The experimental framework, code and datasets have been made public in a code repository.