Santonu Sarkar

Bio: Santonu Sarkar is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Software as a service & Software system. The author has an hindex of 22, co-authored 125 publications receiving 2048 citations. Previous affiliations of Santonu Sarkar include Jadavpur University & Accenture.

Measuring quality of software modularization

Abstract: Quality of modularization of source code is tested using different perspectives such as a structural modularity perspective, an architectural modularity perspective, a size perspective, and a similarity of purpose perspective. A history of changes in modularization may be kept such that the degree to which given source code is well-modularized can be determined over time. The changes made to the code by individual programmers may be kept, such that the degree to which their coding enhanced or harmed modularization may be tracked.

Classification Framework of MapReduce Scheduling Algorithms

Abstract: A MapReduce scheduling algorithm plays a critical role in managing large clusters of hardware nodes and meeting multiple quality requirements by controlling the order and distribution of users, jobs, and tasks execution. A comprehensive and structured survey of the scheduling algorithms proposed so far is presented here using a novel multidimensional classification framework. These dimensions are (i) meeting quality requirements, (ii) scheduling entities, and (iii) adapting to dynamic environments; each dimension has its own taxonomy. An empirical evaluation framework for these algorithms is recommended. This survey identifies various open issues and directions for future research.

Modularization of a Large-Scale Business Application: A Case Study

Abstract: In industries such as banking, retail, transportation, and telecommunications, large software systems support numerous work processes and develop over many years. Throughout their evolution, such systems are subject to repeated debugging and feature enhancements. Consequently, they gradually deviate from the intended architecture and deteriorate into unmanageable monoliths. To contend with this, practitioners often rewrite the entire application in a new technology or invest considerable time in documenting the code and training new engineers to work on it. However, for very large systems, such approaches are typically impossible to carry out. As an alternative, researchers have proposed several tools to automatically modularize software that's grown to be inadequate in both quality and scalability. This case study describes the modularization approach that one company adopted to reengineer a monolithic banking application beset with maintenance and complexity problems. In this case study, we describe the modularization approach we adopted to address this situation, as well as certain other benefits we unearthed as a result of this reengineering exercise.

System and method for job scheduling optimization

Abstract: A system and computer-implemented method for generating an optimized allocation of a plurality of tasks across a plurality of processors or slots for processing or execution in a distributed computing environment. In a cloud computing environment implementing a MapReduce framework, the system and computer-implemented method may be used to schedule map or reduce tasks to processors or slots on the network such that the tasks are matched to processors or slots in a data locality aware fashion wherein the suitability of node and the characteristics of the task are accounted for using a minimum cost flow function.

Impact analysis of software change requests

Abstract: In one example, a system is provided to determine the impact of implementing a change request on a software program. The system may include an architecture model of the software program that includes components. Each of the components may have attributes that may be used by the system to determine a degree of effort to modify each respective one of the components. Components may be associated with keywords. The system may search the change request for the keywords to identify components that may be impacted by the change request. The system may determine the degree of effort to modify any impacted component based on the architecture model. The system may determine the overall impact on the software program based on the degree of effort determined for the impacted components.

Using Multivariate Statistics

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Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Handbook of software reliability engineering

Abstract: Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.