Programs written in a high-level language such as C and Java, interleaved with declarative database processing and inter-service communication statements are common in Enterprise Information Systems (EIS). IT companies that offer re-engineering services for such EIS rely on manual analysis or code analysis tools to understand the flow of control and data within and across the programs during the reverse-engineering phase. Generally, the code analyzers are developed for commonly used programming languages, and lack support for analyzing database processing and inter-service communication statements. Therefore, such analysis tools cannot be readily re-used for re-engineering projects. In this paper, we share the key challenges of handling program analysis requirements for large embedded Structured Query Language (SQL) applications written in C language. We describe our approach to transform a program written in C and embedded SQL into a plain C program which can be automatically analyzed using any available C code analyzer that has no explicit support for domain specific extensions like SQL. We deployed the data dependency analysis based on our transformation approach as a feature in an industrial code analysis tool. We present the evaluation and results that show a precision improvement for slices as compared to the naïve translation provided by the standard pre-compiler.

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.

Using variables before assigning any values to them are known to result in critical failures in an application. Few compilers warn about the use of some, but not all uses of un-initialized variables. The problem persists, especially in COBOL systems, due to lack of reliable program analysis tools. A critical reason is the presence of large number of control flow paths due to the use of un-structured constructs of the language. We present the problems faced by one of our big clients in his large, COBOL based software system due to the use of un-initialized variables. Using static data and control-flow analysis to detect them, we observed large number of false positives (imprecision) introduced due to the unrealizable paths in the un-structured COBOL code. We propose a solution to address the realizability issue. The solution is based on the summary based function analysis, which is adapted for COBOL Paragraphs and Sections, to handle the perform-through and fall-through control-flow, and is significantly engineered to scale for large programs (single COBOL program extending to tens of thousands of lines). Using this technique, we noted very large reduction, 45% on an average, in the number of false positives for the un-initialized variables.