Related papers: Dealing with Data Challenges when Delivering Data-…
Qualitative data analysis provides insight into the underlying perceptions and experiences within unstructured data. However, the time-consuming nature of the coding process, especially for larger datasets, calls for innovative approaches,…
Software development projects management is a complex endeavor because it requires dealing with numerous unforeseen events that constantly arise along the way and that go against the expectations that had been established at the beginning.…
Background: Software engineering requires both technical skills and creative problem-solving. Blind and low-vision software professionals (BLVSPs) encounter numerous workplace challenges, including inaccessible tools and collaboration…
The importance of continuously incorporating customer feedback in the software development process is well established and firmly grounded in concepts such as agile and DevOps. In large-scale organizations such as Dell Technologies however,…
Domain experts from all fields are called upon, working with data scientists, to explore the use of ML techniques to solve their problems. Starting from a domain problem/question, ML-based problem-solving typically involves three steps: (1)…
With software systems becoming increasingly pervasive and autonomous, our ability to test for their quality is severely challenged. Many systems are called to operate in uncertain and highly-changing environment, not rarely required to make…
The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization…
High-quality computational and data-intensive (CDI) applications are critical for advancing research frontiers in almost all disciplines. Despite their importance, there is a significant gap due to the lack of comprehensive best practices…
Background. As artificial intelligence and AI-powered systems continue to grow, the role of data scientists has become essential in software development environments. Data scientists face challenges related to managing large volumes of data…
Data Minimization (DM) is a privacy practice that requires minimizing the use of user data in software systems. However, continuous privacy incidents that compromise user data suggest that the requirements of DM are not adequately…
Large project overruns and overtime work have been reported in the software industry, resulting in additional expense for companies and personal issues for developers. The present work aims to provide an overview of studies related to time…
Our decision-making processes are becoming more data driven, based on data from multiple sources, of different types, processed by a variety of technologies. As technology becomes more relevant for decision processes, the more likely they…
The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in…
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…
Energy systems generate vast amounts of data in extremely short time intervals, creating challenges for efficient data management. Traditional data management methods often struggle with scalability and accessibility, limiting their…
In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when no real data is available (e.g., due to privacy regulations).…
Within the agile paradigm, many software product companies create cross-functional product development teams that own their product or a defined set of product features. In contrast to development teams operating within a…
The rapid growth in terms of the availability of transportation data provides great potential for the introduction of emerging data-driven methodologies into transportation-related research and development efforts. However, advanced…
The damage caused by cybercrime makes the development of secure software inevitable. Although many tools and frameworks exist to support the development of secure software, statistics on cybercrime show no improvement in recent years. To…
Architecture decision making is considered one of the most challenging cognitive tasks in software development. The objective of this study is to explore the state of the practice of architecture decision making in software teams, including…