Benchmark-Based Aggregation of Metrics to Ratings

Alves, Tiago L.; Correia, José Pedro; Visser, Joost

doi:10.1109/iwsm-mensura.2011.15

Cited by 30 publications

(30 citation statements)

References 12 publications

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“…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. They also improved the work to include the calibration of benchmark-based thresholds from code to system level (Alves et al 2011). Fontana et al (2015 worked on an algorithm to automatically identify these 3 percentiles.…”

Section: Detection Strategiesmentioning

confidence: 99%

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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Section: Detection Strategiesmentioning

confidence: 99%

Code smells for Model-View-Controller architectures

et al. 2017

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“…Therefore, we opted for a benchmark based aggregation approach. Given the power-law distribution of the data (see Figure 2) we use the risk based aggregation approach of Alves et al [13] [14].…”

Section: Dependency Freshness At the System-levelmentioning

confidence: 99%

Measuring Dependency Freshness in Software Systems

Cox

Bouwers

Eekelen

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Abstract-Modern software systems often make use of thirdparty components to speed-up development and reduce maintenance costs. In return, developers need to update to new releases of these dependencies to avoid, for example, security and compatibility risks. In practice, prioritizing these updates is difficult because the use of outdated dependencies is often opaque. In this paper we aim to make this concept more transparent by introducing metrics to quantify the use of recent versions of dependencies, i.e. the system's "dependency freshness".We propose and investigate a system-level metric based on an industry benchmark. We validate the usefulness of the metric using interviews, analyze the variance of the metric through time, and investigate the relationship between outdated dependencies and security vulnerabilities. The results show that the measurements are considered useful, and that systems using outdated dependencies four times as likely to have security issues as opposed to systems that are up-to-date.

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“…Calibration was done against a benchmark, following the methodology that was also used to calibrate the SIG quality model [67], [55]. …”

Section: Calibrationmentioning

confidence: 99%

“…In a similar fashion to the SIG quality model (Section 2.3 and [67], [55]), we want to come to a star rating with a 5-point scale for the test code quality of software systems. We define the quality levels so that they correspond to a < 5, 30, 30, 30, 5 > percentage-wise distribution of the systems in the benchmark.…”

Section: Risk Categories and Quality Ratingsmentioning

confidence: 99%

Test Code Quality and Its Relation to Issue Handling Performance

Athanasiou¹,

Nugroho²,

Visser

et al. 2014

IIEEE Trans. Software Eng.

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Abstract-Automated testing is a basic principle of agile development. Its benefits include early defect detection, defect cause localization and removal of fear to apply changes to the code. Therefore, maintaining high quality test code is essential. This study introduces a model that assesses test code quality by combining source code metrics that reflect three main aspects of test code quality: completeness, effectiveness and maintainability. The model is inspired by the Software Quality Model of the Software Improvement Group which aggregates source code metrics into quality ratings based on benchmarking. To validate the model we assess the relation between test code quality, as measured by the model, and issue handling performance. An experiment is conducted in which the test code quality model is applied to 18 open source systems. The test quality ratings are tested for correlation with issue handling indicators, which are obtained by mining issue repositories. In particular, we study the (1) defect resolution speed, (2) throughput and (3) productivity issue handling metrics. The results reveal a significant positive correlation between test code quality and two out of the three issue handling metrics (throughput and productivity), indicating that good test code quality positively influences issue handling performance.

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Benchmark-Based Aggregation of Metrics to Ratings

Cited by 30 publications

References 12 publications

Code smells for Model-View-Controller architectures

Code smells for Model-View-Controller architectures

Measuring Dependency Freshness in Software Systems

Test Code Quality and Its Relation to Issue Handling Performance

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