Automation of merging in ERP revision control

Abstract

In this paper, we describe a model for extracting rules that describe enterprise resource planning (ERP) system upgrade process. The rules are extracted automatically by analyzing programming code from completed ERP systems upgrade projects. Later those rules are verified and tuned by experienced programmer. The rules that we are extracting are described in the language equivalent to the first-order logic (i.e. expressivity of Turing machine). Nevertheless, we put a constrain on the rules description language by defining how knowledge base is used to define these rules. We require that ERP system code and ERP upgrade knowledge base must be transformed to a series of aligned strings without lost of expressivity. Such strong requirement ensures that we are able to use existing machine learning algorithms in the process of software development and upgrade. These series of strings are compared by strings manipulation algorithms and then differences are resolved by merge algorithm presented in this paper. We used Microsoft Dynamics NAV as an example to test usefulness of presented method but other systems can be used as well if they can be presented as series of code strings.