An empirical study into class testability

Bruntink, Magiel; Deursen, Arie van

doi:10.1016/j.jss.2006.02.036

Cited by 113 publications

(118 citation statements)

References 13 publications

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“…Bruntink et al [7,17] investigate factors of testability of OO software systems. Testability is investigated from the perspective of unit testing.…”

Section: Software Testabilitymentioning

confidence: 99%

“…In order to quantify a JUnit test class (C t ) corresponding to a software class (C s ), we used the following two metrics [7,17]: 1) TLoc: This metric gives the number of lines of code of a test class C t . It is used to indicate the size of the test suite corresponding to a software class C s ; 2) TAss: This metric gives the number of invocations of JUnit assert methods that occur in the code of a test class C t .…”

Section: Test Case Metricsmentioning

confidence: 99%

“…These metrics have been introduced by Bruntink et al in [7,17] to indicate the size of a test suite. Bruntink et al based, in fact, the definition of these metrics on the work of Binder [29].…”

Section: Test Case Metricsmentioning

confidence: 99%

“…ing, cohesion and inheritance) were already used in recent years to assess (predict) testability of OO software systems [7,8,[14][15][16][17][18][19][20]. Software testability has been addressed from different point of views.…”

Section: Introductionmentioning

confidence: 99%

“…We performed an empirical analysis using data collected from three open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used the suite of test case metrics introduced by Bruntink et al [7,17] to quantify the corresponding JUnit test cases. These metrics were used, in fact, to classify the classes (in terms of required testing effort) in two categories: high and low.…”

Section: Introductionmentioning

confidence: 99%

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Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Badri¹,

Touré²

2012

JSEA

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In this paper, we investigate empirically the relationship between object-oriented design metrics and testability of classes. We address testability from the point of view of unit testing effort. We collected data from three open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required unit testing effort, in two categories: high and low. In order to evaluate the relationship between object-oriented design metrics and unit testing effort of classes, we used logistic regression methods. We used the univariate logistic regression analysis to evaluate the individual effect of each metric on the unit testing effort of classes. The multivariate logistic regression analysis was used to explore the combined effect of the metrics. The performance of the prediction models was evaluated using Receiver Operating Characteristic analysis. The results indicate that: 1) complexity, size, cohesion and (to some extent) coupling were found significant predictors of the unit testing effort of classes and 2) multivariate regression models based on object-oriented design metrics are able to accurately predict the unit testing effort of classes.

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“…Bruntink et al [7,17] investigate factors of testability of OO software systems. Testability is investigated from the perspective of unit testing.…”

Section: Software Testabilitymentioning

confidence: 99%

Section: Test Case Metricsmentioning

confidence: 99%

Section: Test Case Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Badri¹,

Touré²

2012

JSEA

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Learning to predict test effectiveness

Zakeri‐Nasrabadi

Parsa

2021

Int J of Intelligent Sys

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The high cost of the test can be dramatically reduced, provided that the coverability as an inherent feature of the code under test is predictable. This article offers a machine learning model to predict the extent to which the test could cover a class in terms of a new metric called Coverageability. The prediction model consists of an ensemble of four regression models. The learning samples consist of feature vectors, where features are source code metrics computed for a class. The samples are labeled by the Coverageability values computed for their corresponding classes. We offer a mathematical model to evaluate test effectiveness in terms of size and coverage of the test suite generated automatically for each class. We extend the size of the feature space by introducing a new approach to define submetrics in terms of existing source code metrics. Using feature importance analysis on the learned prediction models, we sort sources code metrics in the order of their impact on the test effectiveness. As a result of which we found the class strict cyclomatic complexity as the most influential source code metric. Our experiments with our prediction models on a large corpus of Java projects containing about 23,000 classes demonstrate the Mean Absolute Error (MAE) of 0.032, Mean-Squared Error (MSE) of 0.004, and an R 2 score of 0.855. Compared with the state-of-the-art coverage prediction models, our models improve MAE, MSE, and an R 2 score by 5.78%, 2.84%, and 20.71%, respectively.

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