Related papers: An architectural technical debt index based on mac…
Architectural Technical Debt (ATD) is considered as the most significant type of TD in industrial practice. In this study, we interview 21 software engineers and architects to investigate a specific type of ATD, namely architectural smells…
Technical debt has become a common metaphor for the accumulation of software design and implementation choices that seek fast initial gains but that are under par and counterproductive in the long run. However, as a metaphor, technical debt…
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…
Technical debt occurs in many different forms across software artifacts. One such form is connected to software architectures where debt emerges in the form of structural anti-patterns across architecture elements, namely, architecture…
To effectively manage Technical Debt (TD), we need reliable means to quantify it. We conducted a Systematic Mapping Study (SMS) where we identified TD quantification approaches that focus on different aspects of TD. Some approaches base the…
Technical Debt (TD) refers to non-optimal decisions made in software projects that may lead to short-term benefits, but potentially harm the system's maintenance in the long-term. Technical debt management (TDM) refers to a set of…
Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies…
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…
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems,…
Enterprise Architecture Debt (EA Debt) arises from suboptimal design decisions and misaligned components that can degrade an organization's IT landscape over time. Early indicators, Enterprise Architecture Smells (EA Smells), are currently…
Background. Architectural smells and code smells are symptoms of bad code or design that can cause different quality problems, such as faults, technical debt, or difficulties with maintenance and evolution. Some studies show that code…
Background: With the rising popularity of Artificial Intelligence (AI), there is a growing need to build large and complex AI-based systems in a cost-effective and manageable way. Like with traditional software, Technical Debt (TD) will…
Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing…
As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more complex team compositions and heavier data dependency. These…
Background: Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One of the key elements to gain effective software defect…
Architectural code smells erode software maintainability and are costly to repair manually, yet unlike localized bugs, they require cross-module reasoning about design intent that challenges both developers and automated tools. While large…
Microservice architectures provide an intuitive promise of high maintainability and evolvability due to loose coupling. However, these quality attributes are notably vulnerable to technical debt (TD). Few studies address TD in microservice…
Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural network (DNN) and then training it on a dataset. This process…
Context: A substantial amount of work has been done to detect smells in source code using metrics-based and heuristics-based methods. Machine learning methods have been recently applied to detect source code smells; however, the current…
Angular is one of the most widely adopted frameworks for developing large-scale, dynamic web applications. As projects increase in scope and complexity, developers face growing challenges in managing architecture and maintaining clean,…