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Practitioners building classifiers often start with a smaller pilot dataset and plan to grow to larger data in the near future. Such projects need a toolkit for extrapolating how much classifier accuracy may improve from a 2x, 10x, or 50x…

Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0.1\% change can cause an overwhelming number of false positives. However,…

Just because software developers say they believe in "X", that does not necessarily mean that "X" is true. As shown here, there exist numerous beliefs listed in the recent Software Engineering literature which are only supported by small…

Agile software development methodologies focus on software projects which are behind schedule or highly likely to have a problematic development phase. In the last decade, Agile methods have transformed from cult techniques to mainstream…

A trend across most areas where simulation-driven development is used is the ever increasing size and complexity of the systems under consideration, pushing established methods of modeling and simulation towards their limits. This paper…

[Context] The use of defect prediction models, such as classifiers, can support testing resource allocations by using data of the previous releases of the same project for predicting which software components are likely to be defective. A…

Software project management makes extensive use of predictive modeling to estimate product size, defect proneness and development effort. Although uncertainty is acknowledged in these tasks, fuzzy inference systems, designed to cope well…

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…

Many see retrospectives as the most important practice of agile software development. Previous studies of retrospectives have focused on pro- cess and outcome at team level. In this article, we study how a large-scale agile development…

Measuring and evaluating software quality has become a fundamental task. Many models have been proposed to support stakeholders in dealing with software quality. However, in most cases, quality models do not fit perfectly for the target…

Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among…

Smaller software companies, such as start-ups do not often follow an explicit process, but rather develop in a more or less unstructured way. Especially when they grow or customer involvement increases. This development without any…

We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks. Our exploration reveals that while scaling parameters consistently yields performance…

Crowdsourcing and data mining can be used to effectively reduce the effort associated with the partial replication and enhancement of qualitative studies. For example, in a primary study, other researchers explored factors influencing the…

Project Management process plays a significant role in effective development of software projects. Key challenges in the project management process are the estimation of time, cost, defect count, and subsequently selection of apt…

Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another. The community choice of automatic metric guides research directions and industrial…

Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However, recent work revealed they also exhibit label bias -- an…

Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning dataset can be reliably predicted prior to training using…

Defect prediction can be a powerful tool to guide the use of quality assurance resources. In recent years, many researchers focused on the problem of Cross-Project Defect Prediction (CPDP), i.e., the creation of prediction models based on…

Fueled by motion prediction competitions and benchmarks, recent years have seen the emergence of increasingly large learning based prediction models, many with millions of parameters, focused on improving open-loop prediction accuracy by…