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Context: Surveys constitute an valuable tool to capture a large-scale snapshot of the state of the practice. Apparently trivial to adopt, surveys hide, however, several pitfalls that might hinder rendering the result valid and, thus,…
Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a…
From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable…
Software effort estimation (SEE) is a core activity in all software processes and development lifecycles. A range of increasingly complex methods has been considered in the past 30 years for the prediction of effort, often with mixed and…
Context: Organizations opt for continuous delivery of incremental updates to deal with uncertainty and minimize waste. However, applying continuous engineering (CSE) practices requires a continuous feedback loop with input from customers…
Multiple intriguing problems are hovering in adversarial training, including robust overfitting, robustness overestimation, and robustness-accuracy trade-off. These problems pose great challenges to both reliable evaluation and practical…
Decision support systems like computer-aided energy system analysis (ESA) are considered one of the main pillars for developing sustainable and reliable energy transformation strategies. Although today's diverse tools can already support…
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods,…
Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at…
High data quality is fundamental for today's AI-based systems. However, although data quality has been an object of research for decades, there is a clear lack of research on potential data quality issues (e.g., ambiguous, extraneous…
Effort estimation models are a fundamental tool in software management, and used as a forecast for resources, constraints and costs associated to software development. For Free/Open Source Software (FOSS) projects, effort estimation is…
Traditional data quality control methods are based on users experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy. Utilizing…
Software quality is one of the essential aspects of a software. With increasing demand, software designs are becoming more complex, increasing the probability of software defects. Testers improve the quality of software by fixing defects.…
Software engineers spend a substantial amount of time using Web search to accomplish software engineering tasks. Such search tasks include finding code snippets, API documentation, seeking help with debugging, etc. While debugging a bug or…
Event data are prevalent in diverse domains such as financial trading, business workflows and industrial IoT nowadays. An event is often characterized by several attributes denoting the meaning associated with the corresponding occurrence…
The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective dataset management, including tasks such as data cleanup,…
Dataset Search -- the process of finding appropriate datasets for a given task -- remains a critical yet under-explored challenge in data science workflows. Assessing dataset suitability for a task (e.g., training a classification model) is…
Software product lines have recently been presented as one of the best promising improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the problems of…
Software Engineering as an industry is highly diverse in terms of development methods and practices. Practitioners employ a myriad of methods and tend to further tailor them by e.g. omitting some practices or rules. This diversity in…
Background: Research software is crucial for enabling research discoveries and supporting data analysis, simulation, and interpretation across domains. However, evolving requirements, complex inputs, and legacy dependencies hinder the…