Test Case Prioritization Based on Neural Network Classification with Artifacts Traceability

Regression testing is an important factor in ensuring software system reliability once new changes are introduced, but maintaining complex testing suites in continuous integration environments is challenging. Test case prioritization techniques are a potential solution to this problem by computing a reordered testing suite that can provide better fault detection capabilities. However, current methods rely on manually providing artifact dependencies (requirements to code, code to test cases, test cases to faults) as input. The purpose of this paper is to minimize the gap between automatic dependency computation and test case prioritization by analyzing how Behavior-Driven Development (BDD) practices affect the two tasks. Thus, the first contribution of this paper is related to the design and implementation of an automatic traceability component to retrieve dependencies based on BDD artifacts (requirements, source code, test cases, and faults). The second contribution refers to the integration of the discovered traces as features in a neural network classification model for test cases for further prioritization. Various architectures were used for the neural network classification model. Two real-world BDD projects were used for the validation of the models, comparing the best-performing models with a baseline test case prioritization technique to assess their fault-detection capabilities. Our approach achieved promising fault detection rates that demonstrate the efficiency of automatic traceability and may lead to future applicability to large-scale projects.