Testability-Driven Development: An Improvement to the TDD Efficiency

Abstract

Abstract

Test-first development (TFD) is a software development approach involving automated tests before writing the actual code. TFD offers many benefits, such as improving code quality, reducing debugging time, and enabling easier refactoring. However, TFD also poses challenges and limitations, requiring more effort and time to write and maintain test cases, especially for large and complex projects. Refactoring for testability is improving the internal structure of source code to make it easier to test. Refactoring for testability can reduce the cost and complexity of software testing and speed up the test-first life cycle. However, measuring testability is a vital step before refactoring for testability, as it provides a baseline for evaluating the current state of the software and identifying the areas that need improvement. This paper proposes a mathematical model for calculating class testability based on test effectiveness and effort and a machine-learning regression model that predicts testability using source code metrics. It also introduces a testability-driven development (TsDD) method that conducts the TFD process toward developing testable code. TsDD focuses on improving testability and reducing testing costs by measuring testability frequently and refactoring to increase testability without running the program. Our testability prediction model has a mean squared error of 0.0311 and an R2 score of 0.6285. We illustrate the usefulness of TsDD by applying it to 50 Java classes from three open-source projects. TsDD achieves an average of 77.81% improvement in the testability of these classes. Experts’ manual evaluation confirms the potential of TsDD in accelerating the TDD process.