Related papers: A Taxonomy of Data Quality Challenges in Empirical…
Data quality describes the degree to which data meet specific requirements and are fit for use by humans and/or downstream tasks (e.g., artificial intelligence). Data quality can be assessed across multiple high-level concepts called…
Software quality in use comprises quality from the user's perspective. It has gained its importance in e-government applications, mobile-based applications, embedded systems, and even business process development. User's decisions on…
High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for…
Data quality is a key element for building and optimizing good learning models. Despite many attempts to characterize data quality, there is still a need for rigorous formalization and an efficient measure of the quality from available…
Within the growing domain of software engineering in the automotive sector, the number of used tools, processes, methods and languages has increased distinctly in the past years. To be able to choose proper methods for particular…
Ensuring high-quality software requires considering the social climate within which the applications will be deployed and used. This can be done by designing quality goals and objectives that are consistent with changing social and ethical…
Objective: To present an overview on the current state of the art concerning metrics-based quality evaluation of software components and component assemblies. Method: Comparison of several approaches available in the literature, using a…
Quality requirements is a difficult concept in software projects, and testing software qualities is a well-known challenge. Without proper management of quality requirements, there is an increased risk that the software product under…
Over the last decades, a considerable amount of empirical knowledge about the efficiency of defect-detection techniques has been accumulated. Also a few surveys have summarised those studies with different focuses, usually for a specific…
The experimental evaluation of the methods and concepts covered in software engineering has been increasingly valued. This value indicates the constant search for new forms of assessment and validation of the results obtained in Software…
Software testing is a mandatory activity in any serious software development process, as bugs are a reality in software development. This raises the question of quality: good tests are effective in finding bugs, but until a test case…
Software metrics offer a quantitative basis for predicting the software development process. In this way, software quality can be improved very easily. Software quality should be achieved to satisfy the customer with decreasing the software…
Deploying successful software-reliant systems that address their mission goals and user needs within cost, resource, and expected quality constraints require design trade-offs. These trade-offs dictate how systems are structured and how…
Effective data processing depends on the quality of the underlying data. However, quality issues such as inconsistencies and uncertainties, can significantly impede the processing and subsequent use of data. Despite the centrality of data…
Software product quality can be defined as the features and characteristics of the product that meet the user needs. The quality of any software can be achieved by following a well defined software process. These software process results…
Software testing has often to be done under severe pressure due to limited resources and a challenging time schedule facing the demand to assure the fulfillment of the software requirements. In addition, testing should unveil those software…
Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example,…
The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A…
Degradation models play a critical role in quality engineering by enabling the assessment and prediction of system reliability based on data. The objective of this paper is to provide an accessible introduction to degradation models. We…
The research discusses how (open) data quality could be described, what should be considered developing a data quality management solution and how it could be applied to open data to check its quality. The proposed approach focuses on…