System for meta-data analysis using prediction based constraints for detecting inconsistences in release process with auto-correction

The Software product release build process usually involves posting a lot of artifacts that are shipped or used as part of the Quality Assurance or Quality Engineering. All the artifacts that are shared or posted together constitute a successful build that can be shipped out. Sometimes, a few of the artifacts might fail to be posted to a shared location that might need an immediate attention in order to repost the artifact with manual intervention. A system and process is implemented for analyzing metadata generated by an automated build process to detect inconsistencies in generation of build artifacts. The system analyzes data retrieved from meta-data streams, once the start of an expected metadata stream is detected the system generates a list of artifacts that the build is expected to generate, based on the prediction model. Information attributes of the meta-data stream are used for deciding on the anticipated behavior of build. Events are generated based on whether the build data is consistent with the predictions made by the model. The system can enable error detection and recovery in an automated build process. The system can adapt to changing build environment by analyzing data stream for historically relevant data properties.