Journal of Software Engineering Research and Development

Management of Technical Debt in Startups: A Systematic Mapping

Tue, 31 Mar 2026 00:00:00 +0000

This study explores the state of the art of Technical Debt (TD) in software startups, seeking to understand the approaches, methods, and techniques used to manage this issue. TD is a concept that refers to suboptimal technical decisions made to accelerate development, such as implementing code or design that later requires revision to avoid future problems. In startups, where speed and innovation are essential for growth, the pressure to quickly release products often leads to the accumulation of TD. Although this offers short-term benefits, such as faster feature releases, in the long term, it can compromise product quality, increase maintenance costs, and reduce the ability to scale the business. We conducted the study through a systematic literature mapping and analyzed articles from the Scopus, IEEE Xplore, and ACM Digital Library databases. The authors selected fourteen studies published between 2017 and 2024. The analysis revealed that practitioners and researchers still lack standardized practices and tools for efficiently managing TD. Furthermore, the research highlights the need for more empirical studies that consider the specific context of startups, where limited resources and the need for accelerated innovation create unique challenges for TD management. The mapping carried out highlights the fragmentation of current approaches and the lack of a unified framework for managing TD in startups. The authors therefore conclude that there is a need to develop and empirically validate strategic models that integrate technical, human, and business aspects to prove more effective solutions for this specific context.

Refactoring Code Smells in Open Source Projects: A Hands-on Approach to Teaching Software Maintenance

Sat, 02 May 2026 00:00:00 +0000

Code smells are suboptimal structures that undermine software quality. While refactoring is the standard technique to address them, its manual application can degrade code if done without discipline. Despite its importance, refactoring is rarely explored in depth in undergraduate computing courses, creating a gap between academia and industry.
Simultaneously, Open Source Software (OSS) projects offer authentic, hands-on learning environments for software maintenance. To address the academic gap and leverage this opportunity, this paper presents and evaluates a hands-on pedagogical approach for teaching code smell refactoring through student contributions to OSS projects. We implemented this approach in two undergraduate Software Quality and Maintenance courses. Our analysis of students' learning experiences reveals that they recognized quality improvements and the connection between refactoring and testing. However, they faced challenges with code complexity and cross-file changes, which sometimes inadvertently introduced new code smells. Regarding the OSS experience, students reported professional growth but struggled with contribution workflows and receiving feedback from maintainers. Our findings offer valuable insights and propose actionable pedagogical recommendations for educators seeking to integrate advanced software maintenance practices into their curricula by leveraging the real-world environment of OSS.

Specifying Accessibility Requirements with Personas, WCAG and BDD Scenarios: Insights from Two Case Studies

Renata Vinadé, Natalya Goelzer, Sabrina Marczak — Tue, 31 Mar 2026 00:00:00 +0000

Accessibility is essential for inclusive digital experiences, yet it is often overlooked in early stages of software development. This paper presents two empirical studies focused on improving how accessibility requirements are specified. In the first study, undergraduate Software Engineering students explored accessibility principles through guided activities and questionnaires. The second study evaluated a structured method combining Personas, User Stories, Behavior-Driven Development (BDD), and WCAG guidelines. Participants applied the method to specify accessibility features in real scenarios. The findings show that although initial knowledge was limited, structured interventions led to more precise, WCAG-aligned, and testable requirements. These results highlight the value of embedding accessibility into software engineering education and demonstrate the effectiveness of combining user-centered design with formal specification techniques.

Extending the Comparative Study of Anomaly Detection Tools in Software Requirements with ChatGPT

Fábio Rodrigues Pereira, Heitor Costa, Paulo Afonso Parreira Junior — Fri, 08 May 2026 00:00:00 +0000

A software requirement indicates a capability or characteristic that a software system must possess to provide value to its stakeholders. It is essential to ensure that the description of the requirements is unambiguous to allow for proper understanding and facilitate its evolution. However, since most software requirements are described in natural language, they may contain subjectivity and inconsistencies in their descriptions, which are conventionally referred to as "Software Requirements Anomalies". Several studies propose tools to aid in the detection of requirements anomalies. However, it can be observed that few of these studies evaluate the effectiveness (recall and precision) of the proposed tools. Therefore, this work presents a comparative study of three anomaly detection tools (RETA, Tactile Check, and Tiger Pro), as well as the ChatGPT model, analyzed based on requirements documents from different domains containing over 85 anomalies. The results show that the Tactile Check tool produced the best performance. Although ChatGPT offers advantages in terms of information visualization and flexibility of interaction, its performance was not satisfactory compared to tools specifically designed for anomaly detection in software requirements. All analyzed tools, including ChatGPT, demonstrated unsatisfactory levels of recall and precision, averaging below 66% and 57%, respectively. These results highlight the need for further contributions in this research area.

TechDebt Tracker: Towards a Method for Monitoring Technical Debt in Agile Projects

Mayra Pereira, Adriana Damasceno — Mon, 11 May 2026 00:00:00 +0000

Context: Software companies that adopt agile methods face numerous challenges in sustaining the long-term evolution of software systems. Technical debt is a key contributor to poor maintainability, often leading to failures in agile software projects. This situation becomes even more problematic when managers do not adequately address technical debt items. Objective: This paper proposes the TechDebt Tracker, a method for supporting the documentation and monitoring of technical debt items within technical debt management activities in the context of agile projects. Method: We employed Design Science Research to develop and evaluate the proposal, following the summarized steps of related work review, problem definition, design and development, demonstration, and evaluation. In the related work review, we examined studies with similar research questions across multiple digital libraries. During the design and development phase, we used Design Thinking, the Business Model Canvas, and the Value Proposition Canvas to identify vulnerabilities and opportunities for improvement in the emerging solution. The proposal was demonstrated in a small software company, from which feedback was gathered to refine the method. The evaluation phase consisted of a small-scale study assessed through questionnaires. Results: The proposal comprises three components: two formulas, a kanban board, and a flow. The formulas are used to measure the impact of technical debt on the project and incorporate data such as the developer’s hourly rate, severity, and penalty associated with each technical debt item. The kanban board includes several columns—such as monitoring, technical debt backlog, and testing—as well as a card template used to register and prioritize each technical debt item. The flow consists of states and actions that, when used together with the kanban board, define a method for monitoring technical debt. The company evaluated the proposal positively, highlighting its technical adequacy. Conclusions: We recommend evaluating the proposal in additional contexts and hope that this proposal will be adopted by software companies that employ agile methodologies in diverse scenarios. Our goal is to take a step toward developing a method that can be used in the daily operations of companies to simplify technical debt management.

Democratizing AI Development: A Feature-Based Categorization of API Platforms, Development Frameworks, LCNC and AIaaS Platforms for LLM-Based Applications

Fri, 08 May 2026 00:00:00 +0000

In recent years, LLM-based AI development platforms have gained widespread adoption, enabling both IT professionals and citizen developers to create AI-powered applications. However, the landscape remains fragmented, with a variety of API-based platforms, AI development frameworks, Low Code/No Code (LCNC) platforms, and Domain-Specific AI as a Service (AIaaS) solution, each offering varying levels of accessibility and customization. Due to the recency of the interest in LLM-based AI development platforms, there is limited systematic research categorizing these tools based on their functionalities and intended user groups. This paper addresses this gap by proposing a structured, feature-based categorization framework, distinguishing between platforms based on criteria such as primary target group, degree of customization, and level of abstraction. Methodologically, we apply a feature-driven analysis grounded in documented capabilities and design affordances across a representative set of tools, and we operationalize the two core dimensions (customization and abstraction) through an anchored ordinal scoring rubric to produce a visual map of categories and overlaps. However, further empirical research is needed to validate the attitude of users towards the different tools in the categories. By providing a clearer understanding of AI development tools, this research supports more informed decision-making and contributes to the democratization of AI adoption across industries.