Metrics and Antipatterns for Software Quality Evaluation

Fontana, Francesca Arcelli; Maggioni, Stefano

doi:10.1109/sew.2011.13

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Hence, we can find could be used to outline the presence of some specific architectural smells, as those described by Garcia; • take into account the evolution of software projects, both as a technique for detecting existing code smells, e.g., Divergent Change, or to define new smells capturing the erosion of the architecture of the entire project or its individual modules; • consider also structural antipatterns as those outlined in the Introduction, where their detection is essentially based on dependency metrics computation. We have already developed the automatic tool support to detect these antipatterns [7]. Moreover, as we outlined in the Introduction, according to the interest a maintainer/developer could have in improving a particular category of metric (e.g., cohesion), he can take into account some smells prior than to others.…”

Section: Discussion On Ongoing Researchmentioning

confidence: 99%

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Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations

Fontana¹,

Ferme²,

Zanoni³

2015

2015 IEEE/ACM 2nd International Workshop on Software Architecture and Metrics

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We can evaluate software architecture quality using a plethora of metrics proposed in the literature, but interpreting and exploiting in the right way these metrics is not always a simple task. This is true for both fixing the right metric threshold values and determining the actions to be taken to improve the quality of the system. Instead of metrics, we can detect code or architectural anomalies that give us useful hints on the possible architecture degradation. In this paper, we focus our attention on the detection of code smells and in particular on their relations and co-occurrences, with the aim to evaluate tecnical debt in an architectural context. We start from the assumption that certain patterns of code anomalies tend to be better indicators of architectural degradation than simple metrics evaluation.

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Section: Discussion On Ongoing Researchmentioning

confidence: 99%

“…Structural antipatterns help to identify components of a system that are critical in terms of their structure, i.e., in terms of their number of outgoing and incoming relationships with the rest of the system. Examples of antipatterns include butterfly, hub, breakable and tangle antipatterns (see for their description [7], [8]). …”

Section: Introductionmentioning

confidence: 99%

Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations

Fontana¹,

Ferme²,

Zanoni³

2015

2015 IEEE/ACM 2nd International Workshop on Software Architecture and Metrics

Self Cite

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“…This is the case, for example, for the God Class design disharmony [31], which is similar to the Large Class code smell defined by Fowler [4] or to the Blob antipattern [32]. Cyclic Dependency is called an architectural smell [33], but corresponds to the Tangle antipattern [34] and to the Cyclically-Dependent Modularization design smell [35], or is considered an architectural violation [36].…”

Section: Related Workmentioning

confidence: 99%

Are Architectural Smells Independent from Code Smells? An Empirical Study

Arcelli¹,

Lenarduzzi²,

Roveda³

et al. 2019

Preprint

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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 smells and architectural smells often appear together in the same file. The correlation between code smells and architectural smells, however, is not clear yet; some studies on a limited set of projects have claimed that architectural smells can be derived from code smells, while other studies claim the opposite.Objective. The goal of this work is to understand whether architectural smells are independent from code smells or can be derived from a code smell or from one category of them. Method. We conducted a case study analyzing the correlations among 19 code smells, six categories of code smells, and four architectural smells. Results. The results show that architectural smells are correlated with code smells only in a very low number of occurrences and therefore cannot be derived from code smells. Conclusion. Architectural smells are independent from code smells, and therefore deserve special attention by researchers, who should investigate their actual harmfulness, and practitioners, who should consider whether and when to remove them.

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“…One of these is our previous study [4], where the two concepts were inversely related, while antipatterns were proportionately related to program faults (or bugs). Another is an investigation by Fontana and Maggioni [8] where they assume the connection and use antipatterns as well as source code metrics to evaluate software quality. Yet another is an empirical study by Yamashita and Moonen [25] where, after the refactoring of 4 Java systems, they conclude that antipatterns could provide experts and developers with more insights into maintainability than source code metrics or subjective judgment alone; however, a combined approach is suggested.…”

Section: Related Workmentioning

confidence: 99%

The Connection between Antipatterns and Maintainability in Firefox

Bán¹

2017

Acta Cybern

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The notion that antipatterns have a detrimental effect on source code maintainability is widely accepted, but there is relatively little objective evidence to support it. We seek to investigate this issue by analyzing the connection between antipatterns and maintainability in an empirical study of Firefox, an open source browser application developed in C++. After extracting antipattern instances and maintainability information from 45 revisions, we looked for correlations to uncover a connection between the two concepts. We found statistically significant negative values for both Pearson and Spearman correlations, most of which were under -0.65. These values suggest there are strong, inverse relationships, thereby supporting our initial assumption that the more antipatterns the source code contains, the harder it is to maintain. Lastly, we combined these data into a table applicable for machine learning experiments, which we conducted using Weka [10] and several of its classifier algorithms. All five regression types we tried had correlation coefficients over 0.77 and used mostly negative weights for the antipattern predictors in the models we constructed. In conclusion, we can say that this empirical study is another step towards objectively demonstrating that antipatterns have an adverse effect on software maintainability.

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Metrics and Antipatterns for Software Quality Evaluation

Cited by 14 publications

References 9 publications

Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations

Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations

Are Architectural Smells Independent from Code Smells? An Empirical Study

The Connection between Antipatterns and Maintainability in Firefox

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