# Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges
## 3 Related Work  
This section discusses the existing research on code smell detection and empirical studies related to Interactive Detection (ID) and Non-Interactive Detection (NID) techniques. Additionally, we highlight the distinctive aspects of our study in comparison to the literature. Code Smell Detection Techniques: Code smells have been extensively studied from various perspectives [4; 5], including their introduction [27], evolution [22], and impact on reliability [19] and maintainability [18]. Numerous techniques for code smell detection have been investigated [2; 3; 5; 28], often relying on heuristics to identify code artifacts affected by specific smell types. While these approaches have shown promising results in empirical assessments, they also exhibit common limitations. For instance, they may detect code smell candidates that developers do not consider actual maintainability problems [26; 29]. Moreover, the agreement between different detectors is often low [3; 25], necessitating the use of diverse techniques for detecting different smells. Additionally, the performance of many detection mechanisms heavily depends on the thresholds used to identify code smell instances [2; 5]. Comparison of ID and NID Techniques: Prior works have evaluated various techniques for code smell detection, but limited attention has been turned to evaluating ID techniques. Studies like those of [PERSON] et al. [29] and [PERSON] et al. [3] have compared different techniques, but they did not include ID in their evaluations. Some research has explored the use of visualization techniques in code smell detection like that of [PERSON] et al. [30] and [PERSON] et al. [5]. However, none of these studies specifically evaluated the application of ID-sensitive visualizations for code smells. Furthermore, while false positives in code smell detection have been studied [25; 26; 31], these evaluations did not consider the ID characteristics of the techniques. Regarding ID evaluations, only one study by [PERSON] et al. [10] assessed the use of an ID technique, focusing on usability guidelines rather than analyzing the effectiveness of code smell detection. Integrating Smell Detection into Software Development Activities: [PERSON] and [PERSON] (2008) [15] introduced a code review tool that empowers developers to track code evolution and anonymously provide positive or negative feedback on specific code segments. Their presented approach addresses various challenges in software review and emphasizes overall code quality enhancement rather than solely concentrating on individual defects. Similarly, [PERSON] and [PERSON] [16] proposed a technical prototype and its integration into a Scrum environment. By means of two experiments with agile software teams and subsequent surveys, they showed that gamification could effectively improve adherence to coding conventions, leading to improvement in code quality. Moreover, [PERSON] et al. [14] developed a novel framework to generate dynamically distributed applications as service chains of components and optimize the life cycles of connected objects. The framework combines a generic context-aware ontology situation model, middleware, and the IoT for managing the user's composite situations at the design and run-time levels. The main differences between this study and the related work are the specific focus, objectives, and methodologies. While this study centers on integrating ID into the Scrum framework for continuous code smell detection in agile software development, related works have explored various aspects of code smell detection from different perspectives. Unlike prior evaluations that mainly compared NID techniques or focused on usability guidelines, this study represents an independent and comprehensive assessment of ID for code smells.