Neural Network Embeddings for Test Case Prioritization.

摘要: In modern software engineering, Continuous Integration (CI) has become an indispensable step towards systematically managing the life cycles of development. Large companies struggle with keeping pipeline updated and operational, in useful time, due to large amount changes addition features, that build on top each other have several developers, working different platforms. Associated such changes, there is always a strong component Testing. As teams projects grow, exhaustive testing quickly becomes inhibitive, becoming adamant select most relevant test cases earlier, without compromising quality. We developed new tool called Neural Network Embeeding for Test Case Prioritization (NNE-TCP) novel Machine-Learning (ML) framework analyses which files were modified when was status transition learns relationships between these tests by mapping them into multidimensional vectors grouping similarity. When are made, more likely be linked prioritized, reducing resources needed find newly introduced faults. Furthermore, NNE-TCP enables entity visualization low-dimensional space, allowing manners similarity reduce redundancies. By applying NNE-TCP, we show first time connection competitive relative traditional methods.