Test Case Prioritization via Embedded Autoencoder Model for Software Quality Assurance

Abstract

Software quality can be ensured by passing the process of software testing before the software is released. However, the software testing process involves many phases which leads to more resources and time consumption. Test Case Prioritization (TCP) has gained widespread acceptance because it prioritizes the tasks and reduces the number of phases and also produces results in good quality software free from defects. The coverage-based prioritization can be useful to distinguish each test case and result in a better prioritization process by using some algorithm. In this work, we propose a coverage-based prioritized test case generation using an Embedded Auto Encoder (EAE) algorithm which will produce an ordered sequence of the prioritized test cases. Initially, the code coverage for each benchmark has been extracted from the source code repository and is further processed to eliminate the noise in the data. The processed data will be given to the embedded autoencoder which consists of an autoencoder and a sparse autoencoder. Once the modeling is done, the model has been trained with the data generated by the Keras Data Generator class. The efficiency of the proposed EAE technique has been evaluated by using the APFD metric and the observations clearly show that the EAE framework proves to provide an APFD metric of 0.72 on average which is a good value in comparison to the previously deployed methodologies.