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

Context Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers (published between 1982 and 2017). Results The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability. Conclusion This paper serves for both researchers and practitioners as an “index” to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects. To assess potential benefits of this review paper, we shared its draft version with two of our industrial collaborators. They stated that they found the review useful and beneficial in their testing activities. Our results can also benefit researchers in observing the trends in this area and identify the topics that require further investigation.

A survey on software testability

We present, in this paper, a metric based testability model for object-oriented programs. The model is, in fact, an adaptation of a model pro-posed in literature for assessing the testability of object-oriented design. The study presented in this paper aims at exploring empirically the capa-bility of the model to assess testability of classes at the code level. We investigate testability from the perspective of unit testing and required testing effort. We designed an empirical study using data collected from two Java software systems for which JUnit test cases exist. To capture testability of classes in terms of required testing effort, we used different metrics to quantify the corresponding JUnit test cases. In order to eva-luate the capability of the model to predict testability of classes (charac-teristics of corresponding test classes), we used statistical tests using correlation.

An empirical analysis of a testability model for object-oriented programs

The aim of this paper is to evaluate empirically the relationship between a new metric (Quality Assurance Indicator--Qi) and testability of classes in object-oriented systems. The Qi metric captures the distribution of the control flow in a system. We addressed testability from the perspective of unit testing effort. We collected data from five open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used different metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required testing effort, in two categories: high and low. In order to evaluate the capability of the Qi metric to predict testability of classes, we used the univariate logistic regression method. The performance of the predicted model was evaluated using Receiver Operating Characteristic (ROC) analysis. The results indicate that the univariate model based on the Qi metric is able to accurately predict the unit testing effort of classes.

/pdf/evaluating-the-effect-of-control-flow-on-the-unit-testing-1mn3f6l1z2.pdf

Evaluating the effect of control flow on the unit testing effort of classes: an empirical analysis

Empirical Analysis for Investigating the Effect of Control Flow Dependencies on Testability of Classes.

For large software systems, testing phase seems to have profound effect on the overall
acceptability and quality of the final product. The success of this activity can be judged by measuring
the testability of the software. A good measure for testability can better manage the testing effort and
time. Different Object Oriented Metrics are used in measurement of object-oriented testability but none
of them is alone sufficient to give an overall reflection of software testability. Thus an integrated
measure considering the effect of all these measures is required to well define the testability. The paper
combines OO software metric values into a single overall value (called Testability Index) that can be
used to calculate the testability of a class. The approach uses fuzzy techniques and concepts
(fuzzification of crisp metric values, inference and aggregation, defuzzification of fuzzy output). We
include empirical data of testing time of 25 different Java classes, which proves that individual metric
values are not sufficient to arrive at the testability of a class and validates the testability index as a
good integrated measure for arriving at the testability of the class.

Vandana Gupta

Papers

A Fuzzy Approach for Integrated Measure of Object-Oriented Software Testability