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Author

Santonu Sarkar

Bio: Santonu Sarkar is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topic(s): Software as a service & Cloud computing. The author has an hindex of 22, co-authored 125 publication(s) receiving 2048 citation(s). Previous affiliations of Santonu Sarkar include Jadavpur University & Accenture.


Papers
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Proceedings ArticleDOI
19 Feb 2008
TL;DR: Preliminary results indicate that LDA is able to identify some of the domain topics and is a satisfactory starting point for further manual refinement of topics, and a human assisted approach based on LDA for extracting domain topics from source code is proposed.
Abstract: One of the difficulties in maintaining a large software system is the absence of documented business domain topics and correlation between these domain topics and source code. Without such a correlation, people without any prior application knowledge would find it hard to comprehend the functionality of the system. Latent Dirichlet Allocation (LDA), a statistical model, has emerged as a popular technique for discovering topics in large text document corpus. But its applicability in extracting business domain topics from source code has not been explored so far. This paper investigates LDA in the context of comprehending large software systems and proposes a human assisted approachbased on LDA for extracting domain topics from source code. This method has been applied on a number of open source and proprietary systems. Preliminary results indicate that LDA is able to identify some of the domain topics and isa satisfactory starting point for further manual refinement of topics

182 citations

Journal ArticleDOI
TL;DR: In this paper, an extensive research study has been carried out with an aim to select the optimum cutting condition with an appropriate wire offset setting in order to get the desired surface finish and dimensional accuracy.
Abstract: This paper presents an investigation on wire electrical discharge machining of γ-titanium aluminide alloy. An extensive research study has been carried out with an aim to select the optimum cutting condition with an appropriate wire offset setting in order to get the desired surface finish and dimensional accuracy. The process has been modeled using additive model in order to predict the response parameters i.e. cutting speed, surface finish and dimensional deviation as function of different control parameters and the main influencing factors are determined for each given machining criteria. Finally, the optimum parametric setting for different machining situation arising out of customer requirements have been synthesized and reported in the paper.

158 citations

Journal ArticleDOI
TL;DR: In this paper, a second-order mathematical model in terms of machining parameters was developed for surface roughness, dimensional shift and cutting speed using response surface methodology (RSM) for wire electrical discharge machining of γ-titanium aluminide.
Abstract: The prediction of optimal machining conditions for required surface finish and dimensional accuracy plays a very important role in process planning of wire electrical discharge machining. The present work deals with the features of trim cutting operation of wire electrical discharge machining of γ-titanium aluminide. A second-order mathematical model, in terms of machining parameters, was developed for surface roughness, dimensional shift and cutting speed using response surface methodology (RSM). The experimental plan was based on the face centered, central composite design (CCD). The residual analysis and experimental results indicate that the proposed models could adequately describe the performance indicators within the limits of the factors that are being investigated. Finally the trim cutting operation has been optimized for a given machining condition by desirability function approach and Pareto optimization algorithm. It was observed that performance of the developed Pareto optimization algorithm is superior compared to desirability function approach.

135 citations

Journal ArticleDOI
TL;DR: In this paper, a feed-forward back-propagation neural network is developed to model the machining process, which is capable of predicting the response parameters as a function of six different control parameters, i.e. pulse on time, pulse off time, peak current, wire tension, dielectric flow rate and servo reference voltage.
Abstract: In the present research, wire electrical discharge machining (WEDM) of γ titanium aluminide is studied. Selection of optimum machining parameter combinations for obtaining higher cutting efficiency and accuracy is a challenging task in WEDM due to the presence of a large number of process variables and complicated stochastic process mechanisms. In general, no perfect combination exists that can simultaneously result in both the best cutting speed and the best surface finish quality. This paper presents an attempt to develop an appropriate machining strategy for a maximum process criteria yield. A feed-forward back-propagation neural network is developed to model the machining process. The three most important parameters – cutting speed, surface roughness and wire offset – have been considered as measures of the process performance. The model is capable of predicting the response parameters as a function of six different control parameters, i.e. pulse on time, pulse off time, peak current, wire tension, dielectric flow rate and servo reference voltage. Experimental results demonstrate that the machining model is suitable and the optimisation strategy satisfies practical requirements.

111 citations

Journal ArticleDOI
TL;DR: A new set of metrics that measure the quality of modularization of a non-object-oriented software system are presented and proposed, based on information-theoretic principles.
Abstract: We present in this paper a new set of metrics that measure the quality of modularization of a non-object-oriented software system. We have proposed a set of design principles to capture the notion of modularity and defined metrics centered around these principles. These metrics characterize the software from a variety of perspectives: structural, architectural, and notions such as the similarity of purpose and commonality of goals. (By structural, we are referring to intermodule coupling-based notions, and by architectural, we mean the horizontal layering of modules in large software systems.) We employ the notion of API (application programming interface) as the basis for our structural metrics. The rest of the metrics we present are in support of those that are based on API. Some of the important support metrics include those that characterize each module on the basis of the similarity of purpose of the services offered by the module. These metrics are based on information-theoretic principles. We tested our metrics on some popular open-source systems and some large legacy-code business applications. To validate the metrics, we compared the results obtained on human-modularized versions of the software (as created by the developers of the software) with those obtained on randomized versions of the code. For randomized versions, the assignment of the individual functions to modules was randomized

106 citations


Cited by
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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

11,850 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

01 Dec 1999

1,636 citations

Book
01 Jan 1978

1,048 citations

Book
Michael R. Lyu1
30 Apr 1996
TL;DR: Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences and best current practice of SRE software reliability measurement experience.
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.

1,039 citations