Detecting architecturally-relevant code anomalies

Ferreira, Manuele; Barbosa, Eiji Adachi; Macia, Isela; Arcoverde, Roberta; Garcia, Alessandro

doi:10.1145/2554850.2555036

Cited by 14 publications

(16 citation statements)

References 9 publications

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“…For more than one decade, researchers have investigated approaches for detecting CAs. 11,25,27,28 Detection strategies are mostly based on the combination of static code metrics and thresholds. Lanza and Marinescu 10 proposed the use of metrics for detecting CAs, reporting a detection accuracy of 60% for the anomalies investigated.…”

Section: Related Workmentioning

confidence: 99%

Exploring architecture blueprints for prioritizing critical code anomalies: Experiences and tool support

et al. 2018

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Summary The manifestation of code anomalies in software systems often indicates symptoms of architecture degradation. Several approaches have been proposed to detect such anomalies in the source code. However, most of them fail to assist developers in prioritizing anomalies harmful to the software architecture of a system. This article presents an investigation on how developers, when supported by architecture blueprints, are able to prioritize architecturally relevant code anomalies. First, we performed a controlled experiment where participants explored both blueprints and source code to reveal architecturally relevant code anomalies. Although the use of blueprints has the potential to improve code anomaly prioritization, the participants often made several mistakes. We found these mistakes might occur because developers miss relationships between implementation and blueprint elements when they prioritize anomalies in an ad hoc manner. Furthermore, the time spent on the prioritization process was considerably high. Aiming to improve the accuracy and effectiveness of the process, we provided means to automate the prioritization process. In particular, we explored 3 prioritization criteria, which establish different ways of relating the blueprint elements with code anomalies. These criteria were implemented in the JSpIRIT tool. The approach was evaluated in the context of 2 applications with satisfactory precision results.

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Section: Related Workmentioning

confidence: 99%

Exploring architecture blueprints for prioritizing critical code anomalies: Experiences and tool support

et al. 2018

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“…In fact, the aforementioned example suggests that the collaborators actually benefit of discussions when identifying a code smell. Thus, the collaborative smell identification emerges as a possible way to improve the e ectiveness of developers otherwise working in isolation, which has been shown to be limited for several reasons, such as the inherent di culty to confirm of refute a code smell suspect (27,44,55,63).…”

Section: Problem Statementmentioning

confidence: 99%

“…Aimed at supporting the identification of code smells, previous work assessed the e ectiveness of individual smell identification, which is performed by single developers (17,55). They usually observed limitations of individual smell identification (27,55), which could be addressed through developers' collaboration. Thus, collaborative smell identification emerges as an opportunity to improve the identification of code smells (63).…”

Section: Background and Related Workmentioning

confidence: 99%

“…According to the literature, developers usually identify code smells individually (27,44,57). By assuming that, studies assess the e ectiveness of individual smell identification and report drawbacks of this identification, such as the di culty of developers to confirm or refute a code smell suspect (65).…”

Section: Individual Vs Collaborative Smell Identification: a Comparatmentioning

confidence: 99%

“…Code elements represent basic units of the software system decomposition, such as classes or methods (30,37). Regardless of the code element a ected by a code smell, its identification is more di cult than it is usually assumed or advertised (27,63). In fact, the identification of code smells in practice consists of three basic steps, as follows (63,65).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

To Collaborate or Not to Collaborate?: Improving the Identification of Code Smells

OLIVEIRA¹

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Characterizing the Architectural Erosion Metrics: A Systematic Mapping Study

et al. 2022

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Software architecture is crucial in determining success or failure in a variety of software development and design fields. Typically, as a system evolves, software architecture deteriorates. This phenomenon is known as architectural erosion. Several studies have addressed architectural erosion based on different solutions. As a result, the metrics technique is the most prevalent solution for architectural erosion. Nevertheless, a comprehensive description of architectural erosion metrics remains unorganized and scattered. This work aims to conduct a systematic mapping to describe and analyze the architectural erosion metrics to provide an overview of erosion metrics and their current trends. Furthermore, no systematic attempts have been made on architectural erosion metrics. The final samples of this study were specified as a total of 43 included papers. Nearly 100 architectural erosion metrics were found. We proposed nine classifications to address architectural erosion challenges, based on adopted approaches in primary studies. The metrics of architectural erosion provide strong evidence for identifying decay and a rapid enabler factor for the adoption of numerous metrics mechanisms to address architectural erosion. The classification of metrics, which is the first of its kind, benefits researchers and practitioners. However, it can be concluded that various aspects are still ambiguous and require further research on architectural erosion measures.

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Detecting architecturally-relevant code anomalies

Cited by 14 publications

References 9 publications

Exploring architecture blueprints for prioritizing critical code anomalies: Experiences and tool support

Exploring architecture blueprints for prioritizing critical code anomalies: Experiences and tool support

To Collaborate or Not to Collaborate?: Improving the Identification of Code Smells

Characterizing the Architectural Erosion Metrics: A Systematic Mapping Study

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