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Background. Technical debt (TD) has long been one of the key factors influencing the maintainability of software products. It represents technical compromises that sacrifice long-term software quality for potential short-term benefits.…
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…
The concept of technical debt has been explored from many perspectives but its precise estimation is still under heavy empirical and experimental inquiry. We aim to understand whether, by harnessing approximate, data-driven,…
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…
Existing software tools enable characterizing and measuring the amount of technical debt at selective granularity levels. In this paper we aim to study the evolution and characteristics of technical debt in open-source software. We carry…
Time-series forecasting has seen significant advancements with the introduction of token prediction mechanisms such as multi-head attention. However, these methods often struggle to achieve the same performance as in language modeling,…
This paper studies the problem of predicting the coding effort for a subsequent year of development by analysing metrics extracted from project repositories, with an emphasis on projects containing XML code. The study considers thirteen…
Context: Previous research on software aging is limited with focus on dynamic runtime indicators like memory and performance, often neglecting evolutionary indicators like source code comments and narrowly examining legacy issues within the…
Technical debt is a metaphor used to convey the idea that doing things in a "quick and dirty" way when designing and constructing a software leads to a situation where one incurs more and more deferred future expenses. Similarly to…
Technical Debt (TD) refers to the long-term costs incurred when developers prioritize short-term delivery over quality-improving work. Architectural Technical Debt (ATD) arises when architectural decisions (e.g., technology choices,…
Defect prediction is one of the most popular research topics due to its potential to minimize software quality assurance efforts. Existing approaches have examined defect prediction from various perspectives such as complexity and developer…
Researchers often delve into the connections between different factors derived from the historical data of software projects. For example, scholars have devoted their endeavors to the exploration of associations among these factors.…
In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer high-performing predictive functionalities, they lack tangible…
Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers…
Technical debt (TD) is a metaphor for code-related problems that arise as a result of prioritizing speedy delivery over perfect code. Given that the reduction of TDs can have long-term positive impact in the software engineering life-cycle…
Technical Debt management is an important aspect in the training of Software Engineering students. In this paper we study the effect of two assessment strategies in an educational context: One based on penalisation, the other based on…
[Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks to productivity, quality, and team morale. Despite growing…
AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the consequences of their uncertain behavior could be severe. In…
Software analytics can be improved by surveying; i.e. rechecking and (possibly) revising the labels offered by prior analysis. Surveying is a time-consuming task and effective surveyors must carefully manage their time. Specifically, they…
Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or statistical techniques that fit past observations. GCMs require substantial computational resources, which…