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.

Functional testing of business applications in the enterprise is carried out by independent test teams. Test scripts are generated manually or automatically from requirements, treating the IT systems as a black box. For every release, when test scripts fail to execute, the test teams need to ascertain the cause of failure, which could be due to mismatch between the requirements and the test models and test scripts, or faults in the test scripts or faults in the source code. The process is cumbersome and time consuming. While several techniques have been developed to localize source code faults, these target testing carried out by the developer. To help test teams localize faults, we propose the novel idea of applying source code based fault localization technique to process models that represent the system functionality. Experimental results show that the techniques when applied to models, were able to localize both test script and source code faults.

Software Programmers need to monitor and measure dynamic behavior of programs. Program instrumentation tools and techniques have aided profiling, debugging, coverage analysis, and dynamic program analysis. While several open source and commercial instrumentation tools are available, that support multitude of techniques and source languages, none of the tools support a cross section of languages. Moreover, instrumentation tools lack support for systems that have been developed using multiple languages. The output produced by each tool is different, leading to problems in usage of the same by other tools to be a challenge. As an IT service provider, our organization maintains systems developed using a large number of programming languages. We develop in-house dynamic program analysis and testing tools, that use instrumentation to help with the maintenance activities. To address the availability as well as the compatibility issues, we propose a novel, generic, model based instrumentation technique. Our technique proposes addition of instrumentation code to a unified programming language model. In this paper, we present GEMS, a generic model based source code instrumentation framework. Language specific parsers in the framework instantiate the unified model. Instrumentation code is added to the model and language pretty printers unparse the model back to appropriate source language. Though the instrumentation code is ultimately added to the source code, the experimental results provided in this paper indicate relatively low execution overhead of about 3-6% over other instrumentation tools.

This paper presents a technique that leverages an existing regression test selection algorithm to compute accurate, updated coverage data on a version of the software, Pi+1, without rerunning any test cases that do not execute the changes from the previous version of the software, Pito Pi+1. The technique also reduces the cost of running those test cases that are selected by the regression test selection algorithm by performing a selective instrumentation that reduces the number of probes required to monitor the coverage data. Users of our technique can avoid the expense of rerunning the entire test suite on Pi+1or the inaccuracy produced by previous approaches that estimate coverage data for Pi+1or that reuse outdated coverage data from Pi. This paper also presents a tool, RECOVER, that implements our technique, along with a set of empirical studies on a set of subjects that includes several industrial programs, versions, and test cases. The studies show the inaccuracies that can exist when an application-regression test selection-uses estimated or outdated coverage data. The studies also show that the overhead incurred by selective instrumentation used in our technique is negligible and overall our technique provides savings over earlier techniques.

One activity performed by developers during regression testing is test-suite augmentation, which consists of assessing the adequacy of a test suite after a program is modified and identifying new or modified behaviors that are not adequately exercised by the existing test suite and, thus, require additional test cases. In previous work, we proposed MATRIX, a technique for test-suite augmentation based on dependence analysis and partial symbolic execution. In this paper, we present the next step of our work, where we (I) improve the effectiveness of our technique by identifying all relevant change-propagation paths, (2) extend the technique to handle multiple and more complex changes, (3) introduce the first tool that fully implements the technique, and (4) present an empirical evaluation performed on real software. Our results show that our technique is practical and more effective than existing test-suite augmentation approaches in identifying test cases with high fault-detection capabilities.

This paper presents a technique that leverages an existing regression test-selection algorithm to compute accurate, updated coverage data on a version of the software, Pi+1, without rerunning any test cases that do not execute the changes from the previous version of the software, Pi, to Pi+1-Users of our technique can avoid the expense of rerunning the entire test suite on Pi+1or the inaccuracy produced by previous approaches that estimate coverage data for Pi+1or reuse outdated coverage data from Pi. This paper also presents a tool, RECOVER, that implements our technique, along with a set of empirical studies. The studies show the inaccuracies that can exist when an application—regression-test selection—uses estimated and outdated coverage data. The studies also show that the overhead incurred by our technique is negligible.