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Technical Debt (TD) identification in software projects issues is crucial for maintaining code quality, reducing long-term maintenance costs, and improving overall project health. This study advances TD classification using…

Technical Debts (TD) are problems of the internal software quality. They are often contracted due to tight project deadlines, for example quick fixes and workarounds, and can make future changes more costly or impossible. TD prevention…

Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks. Currently, there are two popular approaches to make use of…

As the adoption of deep learning techniques in industrial applications grows with increasing speed and scale, successful deployment of deep learning models often hinges on the availability, volume, and quality of annotated data. In this…

Technical debt---design shortcuts taken to optimize for delivery speed---is a critical part of long-term software costs. Consequently, automatically detecting technical debt is a high priority for software practitioners. Software quality…

NonTechnical Debt (NTD) is a common challenge in agile software development, manifesting in four critical forms, Process Debt, Social Debt, People Debt, Organizational debt. NODLA project is a collaboration between Karlstad University and…

The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models. In this paper, we present a new training…

Context: Technical Debt needs to be managed to avoid disastrous consequences, and investigating developers' habits concerning technical debt management is invaluable information in software development. Objective: This study aims to…

The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks. However, the quality of the automatically generated…

Semi-supervised learning leverages unlabeled data to enhance model performance, addressing the limitations of fully supervised approaches. Among its strategies, pseudo-supervision has proven highly effective, typically relying on one or…

Semi-supervised learning approaches train on small sets of labeled data along with large sets of unlabeled data. Self-training is a semi-supervised teacher-student approach that often suffers from the problem of "confirmation bias" that…

Semi-supervised learning provides a solution to reduce the dependency of machine learning on labeled data. As one of the efficient semi-supervised techniques, self-training (ST) has received increasing attention. Several advancements have…

The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data. However, it is rather costly to obtain high-quality human-labeled data, leading to the active research area of training…

Labeling a large set of data is expensive. Active learning aims to tackle this problem by asking to annotate only the most informative data from the unlabeled set. We propose a novel active learning approach that utilizes self-supervised…

Supervised training of optical flow predictors generally yields better accuracy than unsupervised training. However, the improved performance comes at an often high annotation cost. Semi-supervised training trades off accuracy against…

Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training data for each new domain. Zero-shot learning and weak…

Technical debt (TD) refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although developers often informally discuss TD, the concept has not yet crystalized into a…

Intelligent fault diagnosis (IFD) plays a crucial role in ensuring the safe operation of industrial machinery and improving production efficiency. However, traditional supervised deep learning methods require a large amount of training data…

The technical debt (TD) metaphor describes actions made during various stages of software development that lead to a more costly future regarding system maintenance and evolution. According to recent studies, on average 25% of development…

The impact of Technical Debt (TD) on software maintenance and evolution is of great concern, but recent evidence shows that a considerable amount of TD is fixed by the same developers who introduced it; this is termed self-fixed TD. This…