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Surface defect detection is significant in industrial production. However, detecting defects with varying textures and anomaly classes during the test time is challenging. This arises due to the differences in data distributions between…
During code reviews, an essential step in software quality assurance, reviewers have the difficult task of understanding and evaluating code changes to validate their quality and prevent introducing faults to the codebase. This is a tedious…
Defect prediction plays a crucial role in software engineering, enabling developers to identify defect-prone code and improve software quality. While extensive research has focused on refining machine learning models for defect prediction,…
Providing high quality software and evaluating the software reliability in softwarized networks are crucial for vendors and customers. These networks rely on open source code, which are sensitive to contain high number of bugs. Both, the…
Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly…
This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from the historical time series with an efficient…
Software engineering is not an empirically based discipline. Consequently, many of its practices are based on little more than a generally agreed feeling that something may be true. Part of the problem is that it is both relatively young…
Human error research on overconfidence supports the benefits of early visibility of defects and disciplined development. If risk to the enterprise is to be reduced, individuals need to become aware of the reality of the quality of their…
Large organizations have diverse product offerings to meet various business needs. To increase revenue, its common these days to offer software products as integrated product suite(s) rather than individual products. Creating and…
Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and…
Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely large enough to observe the failures that matter. Jones et al. (2025) address this by…
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…
The presence of software vulnerabilities is an ever-growing issue in software development. In most cases, it is desirable to detect vulnerabilities as early as possible, preferably in a just-in-time manner, when the vulnerable piece is…
Conformal prediction is a framework for uncertainty quantification that constructs prediction sets for previously unseen data, guaranteeing coverage of the true label with a specified probability. However, the efficiency of these prediction…
The most common use of data visualization is to minimize the complexity for proper understanding. A graph is one of the most commonly used representations for understanding relational data. It produces a simplified representation of data…
Software security mainly studies vulnerability detection: is my code vulnerable today? This hinders risk estimation, so new approaches are emerging to forecast the occurrence of future vulnerabilities. While useful, these approaches are…
Dynamic random access memory failures are a threat to the reliability of data centres as they lead to data loss and system crashes. Timely predictions of memory failures allow for taking preventive measures such as server migration and…
Performance is a key quality of modern software. Although recent years have seen a spike in research on automated improvement of software's execution time, energy, memory consumption, etc., there is a noticeable lack of standard benchmarks…
This study conducts a benchmarking study, comparing 23 different statistical and machine learning methods in a credit scoring application. In order to do so, the models' performance is evaluated over four different data sets in combination…
Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors. Configuration defects are not uncommon and can result in serious consequences. This paper reports an empirical…