相关论文: Quantifying Technical Debt: A Systematic Mapping S…

Research software (also called scientific software) is essential for advancing scientific endeavours. Research software encapsulates complex algorithms and domain-specific knowledge and is a fundamental component of all science. A pervasive…

While technical debt grows in absolute numbers as software systems evolve over time, the density of technical debt (technical debt divided by lines of code) is reduced in some cases. This can be explained by either the application of…

Knowledge Tracing (KT) is a fundamental technology in intelligent tutoring systems used to simulate changes in students' knowledge state during learning, track personalized knowledge mastery, and predict performance. However, current KT…

Decomposition, statically dividing a program into multiple units, is a common programming technique for realizing parallelism and refining programs. The decomposition of a sequential program into components is tedious, due to the…

A control model is typically classified into three forms: conceptual, mathematical and simulation (computer). This paper analyzes a conceptual modeling application with respect to an inventory management system. Today, most organizations…

Self-Admitted Technical Debt or SATD can be found in various sources, such as source code comments, commit messages, issue tracking systems, and pull requests. Previous research has established the existence of relations between SATD items…

Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance. The objective of KT is to predict a student's…

Background. Code Technical Debt (Code TD) prediction has gained significant attention in recent software engineering research. However, no standardized approach to Code TD prediction fully captures the factors influencing its evolution.…

Quantifying the similarity between datasets has widespread applications in statistics and machine learning. The performance of a predictive model on novel datasets, referred to as generalizability, depends on how similar the training and…

Technical debt describes situations where developers write less-than-optimal code to meet project milestones. However, this debt accumulation often results in future developer effort to live with or fix these quality issues. To better…

Architectural debt is a form of technical debt that derives from the gap between the architectural design of the system as it "should be" compared to "as it is". We measured architecture debt in two ways: 1) in terms of system-wide coupling…

The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses significant challenges in creating robust models. To address this, synthetic data generated through…

We present a different approach to developing a concept of time for specifying temporality in the conceptual modeling of software and database systems. In the database field, various proposals and products address temporal data. The…

We present the Classroom Technology Deployment Matrix (CTDM), a tool for high-level Planning, Monitoring, Evaluating and Reporting of classroom deployments of educational technologies, enabling researchers, teachers and schools to work…

In quantitative program analysis, values are assigned to execution traces to represent a quality measure. Such analyses cover important applications, e.g. resource usage. Examining all traces is well known to be intractable and therefore…

Context: Technical Debt (TD) discusses the negative impact of sub-optimal decisions to cope with the need-for-speed in software development. Code Technical Debt Items (TDI) are atomic elements of TD that can be observed in code artefacts.…

Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and reproducibility of results. Objective: This study aims to…

Modern software is developed under considerable time pressure, which implies that developers more often than not have to resort to compromises when it comes to code that is well written and code that just does the job. This has led over the…

Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains, but their heavy resource demands make quantization-reducing precision to lower-bit formats-critical for efficient serving. While many…

When not appropriately managed, technical debt is considered to have negative effects on the long term success of a software project. However, how the debt metaphor applies to requirements engineering in general, and to requirements…