Data migration from source to target information system is a critical step for modernizing information systems. Central to data migration is data transform that transforms the source system data into target system. In this paper we present a tool that assists the experts in creating the data transformation specification by (a) suggesting candidate field matches between the source and target data models using machine learning and knowledge representation, and (b) rules for the data transformation using program synthesis. It takes the expert’s feedback for the identified matches and synthesized rules and proposes new matches and transformation rules. We have executed our tool on real-life industrial data. Our schema matching recall at 5 is 0.76, while for the rule generator recall at 2 is 0.81.

An enterprise system operates business by providing various services that are guided by set of certain business rules (BR) and constraints. These BR are usually written using plain Natural Language in operating procedures, terms and conditions, and other documents or in source code of legacy enterprise systems. For implementing the BR in a software system, expressing them as UML use-case specifications, or preparing for Merger & Acquisition (M&A) activity, analysts manually interpret the documents or try to identify constraints from the source code, leading to potential discrepancies and ambiguities. These issues in the software system can be resolved only after testing, which is a very tedious and expensive activity. To minimize such errors and efforts, we propose BuRRiTo framework consisting of automatic extraction of BR by mining documents and source code, ability to clean them of various anomalies like inconsistency, redundancies, conflicts, etc. and able to analyze the functional gaps present and performing semantic querying and searching.

In the modern age, the need for automation has led to Business Organizations representing their functionality as structured Business Rules. SBVR has come up as an universally popular format for representation of Business Rules. The presence of different Business Organizations working in a particular real life domain results in generation of different rules for each of the organization. Due to the varying business practices, like mergers & acquisitions, upgrades, incorporation of a new application, etc., it becomes necessary to compare a set of Business Rules of a particular organization with the rules of a reference model, to get a measure of similarity among the business functionality of the two. Presently, this comparison is carried out manually by business experts or by executing the rules of one organization with the data of another and checking if they are compliant. Both the approaches are extremely tedious and expensive as modern organizations have huge rule sets and data sets.We present MatGap, a tool which performs a systematic Match and Gap Analysis between two sets of SBVR-based Business Rules applicable to a specific domain, using Global Vectors(GloVe) model and SMT-LIBv2. The analysis report gives a measure of Match among the rules and entities, thus providing the best alignment and aids to identify the representational Gaps(if any) among the rules and entities. The tool also checks whether the embedded logic in the reference Business Rule set is covered by the other Rule set, thus highlighting the business functionality gap that is present in the latter.

In recent times, approximate computing is being looked at as a viable alternative for reducing the energy consumption of programs, while marginally compromising on the correctness of their computation. The idea behind approximate computing is to introduce approximations at various levels of the execution stack, with an attempt to realize the resource hungry computations on low resource consuming approximate hardware blocks. However approximate computing for program transformation faces a serious challenge of automatically identifying core program areas/statements where approximations can be introduced, with a quantifiable measure of the resulting program correctness compromise. Introducing approximations randomly can cause performance deterioration without much energy advantage, which is undesirable. In this paper, we introduce a verification-guided method to automatically identify program blocks which lend themselves to easy approximations, while not compromising significantly on program correctness. Our method is based on identifying regions of code which are less influential for the computation of the program outputs and therefore, can be compromised with, however still having a potential of significant resource reduction. We take the help of assertions to quantify the effect of the resulting transformations on program outputs. We show experimental results to support our proposal.