Related papers: A Quality Model for Actionable Analytics in Rapid …
Software reusability has become much interesting because of increased quality and reduce cost. A good process of software reuse leads to enhance the reliability, productivity, quality and the reduction of time and cost. Current reuse…
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engineers and researchers who developed the models and the…
Context: Big Data Cybersecurity Analytics is aimed at protecting networks, computers, and data from unauthorized access by analysing security event data using big data tools and technologies. Whilst a plethora of Big Data Cybersecurity…
Manufacturing quality audits are pivotal for ensuring high product standards in mass production environments. Traditional auditing processes, however, are labor-intensive and reliant on human expertise, posing challenges in maintaining…
Test automation is important in software industry but self-assessment instruments for assessing its maturity are not sufficient. The two objectives of this study are to synthesize what an organization should focus to assess its test…
Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training ML…
In this paper two intensive problems faced during software application's analysis and development process arose by the software industry are briefly conversed i.e. identification of fault proneness and increase in rate of variability in the…
Most of today's products and services are software-based. Organizations that develop software want to maintain and improve their competitiveness by controlling software-related risks. To do this, they need to align their business goals with…
The concept of software quality is very complex and has many facets. Reflecting all these facets and at the same time measuring everything related to these facets results in comprehensive but large quality models and extensive measurements.…
Applications of multilevel models usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed.…
Context: Mining software repositories is a popular means to gain insights into a software project's evolution, monitor project health, support decisions and derive best practices. Tools supporting the mining process are commonly applied by…
High data quality is critical for reliable analytics and operational efficiency. A growing ecosystem of tools has emerged to support data quality management, ranging from lightweight open-source libraries to comprehensive enterprise…
Recent advances in the field of Business Process Management have brought about several suites able to model complex data objects along with the traditional control flow perspective. Nonetheless, when it comes to formal verification there is…
Machine learning (ML) is increasingly applied across industries to automate decision-making, but concerns about ethical and legal compliance remain due to limited transparency, fairness, and accountability. Monitoring through logging a…
Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process.…
Business processes are fundamental to organizational operations, yet their optimization remains challenging due to the timeconsuming nature of manual process analysis. Our paper harnesses Large Language Models (LLMs) to automate value-added…
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality,…
Context - The exponential growth of data is becoming a significant concern. Managing this data has become incredibly challenging, especially when dealing with various sources in different formats and speeds. Moreover, Ensuring data quality…
Assessing and improving the quality of data are fundamental challenges for data-intensive systems that have given rise to applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data…
Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions remains a major challenge when QD is used in complex real-world…