Deriving metric thresholds from benchmark data

Alves, Tiago L.; Ypma, Christiaan; Visser, Joost

doi:10.1109/icsm.2010.5609747

Cited by 173 publications

(203 citation statements)

References 18 publications

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“…While other strategies can be used (e.g., Alves et al (2010) and Oliveira et al (2014)), up to now there is no empirical evaluation of which strategy works best.…”

Section: Threats To Validitymentioning

confidence: 99%

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Code smells for Model-View-Controller architectures

et al. 2017

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Previous studies have shown the negative effects that low-quality code can have on maintainability proxies, such as code change-and defect-proneness. One of the symptoms of low-quality code are code smells, defined as sub-optimal implementation choices. While this definition is quite general and seems to suggest a wide spectrum of smells that can affect software systems, the research literature mostly focuses on the set of smells defined in the catalog by Fowler and Beck, reporting design issues that can potentially affect any kind of system, regardless of their architecture (e.g., Complex Class). However, systems adopting a specific architecture (e.g., the Model-View-Controller pattern) can be affected by other types of poor practices that only manifest themselves in the chosen architecture. We present a catalog of six smells tailored to MVC applications and defined by surveying/interviewing 53 MVC developers. We validate our catalog from different perspectives. First, we assess the relationship between the defined smells and the code change-and defect-proneness. Second, we investigate when these smells are introduced and how long they survive. Third, we survey 21 developers to verify their perception of the defined smells. Fourth, since our catalog has been mainly defined together with developers adopting a specific Java framework in their MVC applications (e.g., Spring), we interview four expert developers working with different technologies for the implementation of their MVC applications to check the generalizability of our catalog. The achieved results show that the defined Web MVC smells (i) more often than not, have more chances of being subject to changes and defects, (ii) are mostly introduced when the affected file (i.e., the file containing the smell) is committed for the first time in the repository and survive for long time in the system, (iii) are perceived by developers as severe problems, and (iv) generalize to other languages/frameworks.

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“…While other strategies can be used (e.g., Alves et al (2010) and Oliveira et al (2014)), up to now there is no empirical evaluation of which strategy works best.…”

Section: Threats To Validitymentioning

confidence: 99%

“…Benchmarks are also a common strategy for deriving thresholds. Alves et al (2010) proposed an approach based on weighted functions. Using lines of code as weight, they select the code metric values relative to the 70%, 80%, and 90% percentiles of the accumulated weight, and uses these as thresholds.…”

Section: Detection Strategiesmentioning

confidence: 99%

Code smells for Model-View-Controller architectures

et al. 2017

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“…Alves, Ypma and Visser, in a publication entitled "Deriving metric thresholds from benchmark data" [7], propose thresholds to metrics; values that exceed these thresholds trigger an alarm for more careful, manual, inspection. The authors acknowledge that the distributions of the same metric in distinct project might be vastly different, and meet this challenge with a sophisticated transformation of the numerical values.…”

Section: Related Workmentioning

confidence: 99%

When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

Gil¹,

Lalouche²

2016

JOT

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This paper places its attention on a familiar phenomena: that code metrics such as lines of code are extremely context dependent and their distribution differs from project to project. We apply visual inspection, as well as statistical reasoning and testing, to show that such metric values are so sensitive to context, that their measurement in one project offers little prediction regarding their measurement in another project.On the positive side, we show that context bias can be neutralized, at least for the majority of metrics that we considered, by what we call Log Normal Standardization (LNS). Concretely, the LNS transformation is obtained by shifting (by subtracting the mean) and scaling (by dividing by the standard deviation) of the log of a metric value.Thus, we conclude that the LNS-transformed-, are to be preferred over the plain-, values of metrics, especially in comparing modules from different projects. Conversely, the LNS-transformation suggests that the "context bias" of a software project with respect to a specific metric can be summarized with two numbers: the mean of the logarithm of the metric value, and its standard deviation.

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“…It requires a theory and practical base and it should meet certain requirements. It ought [1]: a) not to be based on expert opinion but on measurement data; b) to respect the statistical properties of the measure, such as metric scale and distribution and to be resilient against outlier values; and c) to be repeatable, transparent and easy to carry out. Some authors have defined thresholds based on experience.…”

Section: Related Work On Thresholds For Business Process Measuresmentioning

confidence: 99%

Quality indicators for business process models from a gateway complexity perspective

Sánchez-González

García

Ruíz

et al. 2012

Information and Software Technology

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Abstract.Context: Quality assurance of business process models has been recognized as an important factor for modeling success at an enterprise level. Since quality of models might be subject to different interpretations, it should be addressed in the most objective way, by the application of measures. That said, however, assessment of measurement results is not a straightforward task: it requires the identification of relevant threshold values, which are able to distinguish different levels of process model quality. Objective: Since there is no consensual technique for obtaining these values, this paper proposes the definition of thresholds for gateway complexity measures based on the application of statistical techniques on empirical data. Method: To this end, we conducted a controlled experiment that evaluates quality characteristics of understandability and modifiability of process models in two different runs. The thresholds obtained were validated in a replication of the experiment. Results: The thresholds for gateway complexity measures are instrumental as guidelines for novice modelers. A tool for supporting business process model measurement and improvement is described, based on the automatic application of measurement, and assessment as well as derivation of advice about how to improve the quality of the model. Conclusion: It is concluded that thresholds classified business process models in the specific level of understandability and modifiability, so these thresholds were good and useful for decision-making.

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Deriving metric thresholds from benchmark data

Cited by 173 publications

References 18 publications

Code smells for Model-View-Controller architectures

Code smells for Model-View-Controller architectures

When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

Quality indicators for business process models from a gateway complexity perspective

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