Related papers: Multi-Artifact Analysis of Self-Admitted Technical…
Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with existing techniques is that, while developers want rationales for…
AI coding assistants are now widely used in software development. Software developers increasingly integrate AI-generated code into their codebases to improve productivity. Prior studies have shown that AI-generated code may contain code…
Successful development of software systems involves efficient navigation among software artifacts. One state-of-practice approach to structure information is to establish trace links between artifacts, a practice that is also enforced by…
Advances in AI have led to new types of technical debt in software engineering projects. AI-based competition platforms face challenges due to rapid prototyping and a lack of adherence to software engineering principles by participants,…
Context: Technical lag accumulates when software systems fail to keep pace with technological advancements, leading to a deterioration in software quality. Objective: This paper aims to consolidate existing research on technical lag,…
Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has…
Although the methodology of Design Science Research (DSR) is playing an increasingly important role with the emergence of the "sciences of the artificial", the validity of the resulting artifacts is occasionally questioned. This paper…
Software documentation often struggles to catch up with the pace of software evolution. The lack of correct, complete, and up-to-date documentation results in an increasing number of documentation defects which could introduce delays in…
Although repeatability and reproducibility are essential in science, failed attempts to replicate results across diverse fields made some scientists argue for a reproducibility crisis. In response, several high-profile venues within…
This paper identifies a model of software evolution that is prevalent in large, long-lived academic research tool suites (3L-ARTS). This model results in an "archipelago" of related but haphazardly organized architectural "islands", and…
Over the past few years, deep learning methods have been applied for a wide range of Software Engineering (SE) tasks, including in particular for the important task of automatically predicting and localizing faults in software. With the…
In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-the-art approaches. It contains about 860K code functions and…
Despite long-standing efforts in accelerating scientific discovery with AI, building AI co-scientists remains challenging due to limited high-quality data for training and evaluation. To tackle this data scarcity issue, we present AutoSDT,…
Background: To adequately attend to non-functional requirements (NFRs), they must be documented; otherwise, developers would not know about their existence. However, the documentation of NFRs may be subject to Technical Debt and Waste, as…
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 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…
Feature extraction is a fundamental task in the application of machine learning methods to SAT solving. It is used in algorithm selection and configuration for solver portfolios and satisfiability classification. Many approaches have been…
Managing technical debt (TD) is essential for maintaining long-term software projects. Nonetheless, the time and cost involved in technical debt management (TDM) are often high, which may lead practitioners to omit TDM tasks. The adoption…
Technical debt is a pervasive problem in software development. Software development teams have to prioritize debt items and determine whether they should address debt or develop new features at any point in time. This paper presents…
Artifact Detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in Critical Care Units (CCU) by assessing quality of data prior to Clinical Event Detection (CED) and Parameter Derivation (PD). This…