Abstract: An automated system for resolving program merges uses a sequence-to-sequence supervised machine learning model trained from developer-resolved merge conflicts to learn to predict a merge resolution to resolve a three-way program merge. The model utilizes an embedding of the merge tuple (A, B, O) which represents the program syntax, program semantics and the intent of the program inputs. The model uses a pointer mechanism to construct the resolved program in terms of the lines of source code found in the input programs.

Abstract: An automated system for resolving program merges uses neural transformers with attention. In one aspect, a neural encoder transformer model is trained from developer-resolved merge conflicts to learn to predict a resolution strategy that aids a developer in constructing a merged program. In a second aspect, a neural decoder transformer model is trained on the syntax and semantics of different source code programming languages to predict a merge resolution consisting of interleaved lines of source code from programs A, B, or O, where programs A and B contain changes to code base O.

Abstract: Systems and methods for predicting a software build error are described herein. In one example, a method includes detecting a plurality of changes in software. The method also includes identifying a plurality of change lists, wherein a change list is identified for each of the plurality of changes in the software. Additionally, the method includes identifying a characteristic for each change list in the plurality of change lists. Furthermore, the method includes calculating a plurality of probabilities based at least in part on the characteristic of each of the plurality of change lists, wherein each of the probabilities indicates the likelihood of one of the plurality of change lists creating the software build error. The method also includes reporting the plurality of probabilities of the software build error.

Abstract: The discussion relates to software development automated analytics. One example can access a database related to a software development project. The database can include both software code and non-code metadata. The example can analyze the non-code metadata and the software code to identify parameters. It can relate the parameters to aspects of the software development project. The example can cause a graphical user interface to be presented that conveys an individual aspect.

Abstract: Systems and methods for predicting a software build error are described herein. In one example, a method includes detecting a plurality of changes in software. The method also includes identifying a plurality of change lists, wherein a change list is identified for each of the plurality of changes in the software. Additionally, the method includes identifying a characteristic for each change list in the plurality of change lists. Furthermore, the method includes calculating a plurality of probabilities based at least in part on the characteristic of each of the plurality of change lists, wherein each of the probabilities indicates the likelihood of one of the plurality of change lists creating the software build error. The method also includes reporting the plurality of probabilities of the software build error.

Abstract: The discussion relates to software development automated analytics. One example can access a database related to a software development project. The database can include both software code and non-code metadata. The example can analyze the non-code metadata and the software code to identify parameters. It can relate the parameters to aspects of the software development project. The example can cause a graphical user interface to be presented that conveys an individual aspect.