Related papers: Estimation and Prediction of technical debt: a pro…
The vast majority of the long tail of scientific software, the myriads of tools that implement the many analysis and visualization methods for different scientific fields, is highly specialized, purpose-built for a research project, and has…
As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments…
Software and IT industry in Pakistan have seen a dramatic growth and success in past few years and is expected to get doubled by 2020, according to a research. Software development life cycle comprises of multiple phases, activities and…
Self-Admitted Technical Debt (SATD) annotates development decisions that intentionally exchange long-term software artifact quality for short-term goals. Recent work explores the existence of SATD clones (duplicate or near duplicate SATD…
In the trend towards tolerating hardware unreliability, accuracy is exchanged for cost savings. Running on less reliable machines, "functionally correct" code becomes risky and one needs to know how risk propagates so as to mitigate it.…
Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a…
Tightly coupled and interdependent systems inhibit productivity by requiring developers to carefully coordinate their changes, even when modifying subsystems that should be independent of one another. Poor architectural decisions frequently…
Vibe Coding (VC) is a form of software development assisted by generative AI, in which developers describe the intended functionality or logic via natural language prompts, and the AI system generates the corresponding source code. VC can…
Quantitative technology forecasting uses quantitative methods to understand and project technological changes. It is a broad field encompassing many different techniques and has been applied to a vast range of technologies. A widely used…
Software and systems traceability is essential for downstream tasks such as data-driven software analysis and intelligent tool development. However, despite the increasing attention to mining and understanding technical debt in software…
Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to…
Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention. In this regard, CDP plays a crucial role in assessing the…
Research software refers to software development tools that accelerate discovery and simplifies access to digital infrastructures. However, although research software platforms can be built increasingly more innovative and powerful than…
Software defect prediction using code metrics has been extensively researched over the past five decades. However, prediction harnessing non-software metrics is under-researched. Considering that the root cause of software defects is often…
One of the prerequisites of any organization is an unvarying sustainability in the dynamic and competitive industrial environment. Development of high quality software is therefore an inevitable constraint of any software industry. Defect…
Making conjectures about future consequences of a technology is an exercise in trying to reduce various forms of uncertainty. Both to produce and reason about these conjectures requires understanding their potential limitations. In other…
Reliable effort estimation remains an ongoing challenge to software engineers. Accurate effort estimation is the state of art of software engineering, effort estimation of software is the preliminary phase between the client and the…
A key aspect of technical debt (TD) management is the ability to measure the amount of principal accumulated in a system. The current literature contains an array of approaches to estimate TD principal, however, only a few of them focus…
The rapid development of new software and algorithms, fueled by the immense amount of data available, has made the shelf life of software products a lot shorter. With a rough estimate of more than 40,000 new software projects developed…
The adoption of Machine and Deep Learning (ML/DL) technologies introduces maintenance challenges, leading to Technical Debt (TD). Algorithm Debt (AD) is a TD type that impacts the performance and scalability of ML/DL systems. A review of 42…