Related papers: A Survey of Data Quality Measurement and Monitorin…
Public space quality assessment lacks systematic methodologies that integrate factors across diverse spatial typologies while maintaining context-specific relevance. Current approaches remain fragmented within disciplinary boundaries,…
Data quality is crucial for training accurate, unbiased, and trustworthy machine learning models as well as for their correct evaluation. Recent works, however, have shown that even popular datasets used to train and evaluate…
Good software documentation encourages good software engineering, but the meaning of "good" documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information…
Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell…
Software code quality is a construct with three dimensions: maintainability, reliability, and functionality. Although many firms have incorporated code quality metrics in their operations, evaluating these metrics still lacks consistent…
In recent years, Software has become an indispensable part of every segment from simple Office Automation to Space Technology and E-mail to E-commerce. The evolution in Software architecture is always an open issue for researchers to…
Abstract- The vision of the Linked Open Data (LOD) initiative is to provide a distributed model for publishing and meaningfully interlinking open data. The realization of this goal depends strongly on the quality of the data that is…
The increasing size and availability of web data make data quality a core challenge in many applications. Principles of data quality are recognized as essential to ensure that data fit for their intended use in operations, decision-making,…
Today's software systems like cyber-physical production systems or big data systems have to process large volumes and diverse types of data which heavily influences the quality of these so-called data-intensive systems. However, traditional…
Datasets serve as crucial training resources and model performance trackers. However, existing datasets have exposed a plethora of problems, inducing biased models and unreliable evaluation results. In this paper, we propose a…
The rising interest in the construction and the quality of (business) process models resulted in an abundancy of emerged research studies and different findings about process model quality. The lack of overview and the lack of consensus…
Nowadays, data is becoming the new fuel for economic wealth and creation of novel and profitable business models. Multitude of technologies are contributing to an abundance of information sources which are already the baseline for…
In the 1990s, statisticians began thinking in a principled way about how computation could better support the learning and doing of statistics. Since then, the pace of software development has accelerated, advancements in computing and data…
Knowledge graphs have been widely adopted in both enterprises, such as the Google Knowledge Graph, and open platforms like Wikidata, to represent domain knowledge and support artificial intelligence applications. They model real-world…
Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process…
The technical state of software, i.e., its technical debt (TD) and maintainability are of increasing interest as ever more software is developed and deployed. Since td and maintainability are neither uniformly defined, not easy to…
Rules based approaches for data quality solutions often use business rules or integrity rules for data monitoring purpose. Integrity rules are constraints on data derived from business rules into a formal form in order to allow…
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…
Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and…
In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels,…