Tata Consultancy Services (TCS) in partnership with ACM India has been organizing the Bebras Challenge in India since 2018. To date, over 500,000 students have participated in the challenge. Tata Steel Foundation (TSF) initiated "The Thousand Schools Program" in 2015, in remote regions of Odisha and Jharkhand to improve children's learning levels and expose them to new knowledge areas and skills. These regions are tribally dense, forested, and have hilly terrain. They are highly underserved in terms of schools and teachers. The majority of the students are first-generation learners with no home support. A large number of them fall out of schooling and get engaged in child labor.

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.

When enterprises outsource maintenance of IT systems to service providers, thorough knowledge acquisition is critical to the success of the engagement. Program comprehension contributes significantly to acquiring knowledge of the IT systems. It is a common practice to execute test scripts to identify critical scenarios in the system and then trace these as flows in the programs. Instead of executing test scripts, we propose the novel idea of mining workflows from test scripts to construct formal process models. The global view provided by the mined model can not only help transition teams gain high level understanding of the system but also help identify critical flows. We also suggest categorization of test cases using supervised learning to improve comprehension.

The benefits offered by cloud technologies have compelled enterprises to adopt the Software-as-a-Service (SaaS) model for their enterprise software needs. A SaaS has to be configured or customized to suit the specific requirements of every enterprise that subscribes to it. IT service providers have to deal with the problem of testing many such configurations created for different enterprises. The software gets upgraded periodically and the configurations need to be tested on an ongoing basis to ensure business continuity. In order to run the testing organization efficiently, it is imperative that the test cycle is automated. Developing automated test scripts for a large number of configurations is a non-trivial task because differences across them may range from a few user interface changes to business process level changes. We propose an approach that combines the benefits of model driven engineering and variability modeling to address this issue. The approach comprises of the Enterprise Software Test Modeling Language to model the test cases. We use the Common Variability Language to model variability in the test cases and apply model transformations on a base model to generate a test model for each configuration. These models are used to generate automated test scripts for all the configurations. We describe the test modelling language and an experiment which shows that the approach can be used to automatically generate variations in automated test scripts.

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.

In industrial test and maintenance projects, test execution plans are important for performing test cycle in a time constrained manner with the objective, of delivering the expected quality by effective utilization of resources. To take advantage of the inherent parallelism in test suites, multiple resources are often deployed to test an application. The resource allocation is however driven more by costs and risks and does not exploit the parallelism. Test execution plans are often static in nature and are not well equipped to handle dynamically occurring events like abends, and changes in resource availability and test requirements. Derivation of test plans is a cumbersome activity, as it also needs to take into account test execution order, violation of which may result in unexpected failures. In this paper, we describe an approach to derive a test execution plan to facilitate parallel execution, given resource availability and test case dependencies. The execution plan provides workload distribution and scheduling of the test cases in a test suite. The case studies on test projects have shown that the derived test plans can contribute significantly towards improving the test execution cycles of the test suites.