N. Narayanan Prasanth

Bio: N. Narayanan Prasanth is an academic researcher from VIT University. The author has contributed to research in topics: Software maintenance & Knapsack problem. The author has an hindex of 2, co-authored 7 publications receiving 13 citations.

Prediction of maintainability using software complexity analysis: An extended FRT

Abstract: In this paper, a method is proposed for predicting software maintainability. Prediction of maintainability of a product is done by using its code complexity. Here a sample of 4 products is taken into consideration and both the absolute and relative complexity assessment are made over it. The process of measuring the code complexity is done at testing phase. We employ the fuzzy repertory table (FRT) technique to acquire the necessary domain knowledge of testers from which the software complexity analysis is made. Regression analysis is then used to predict maintainability from the product's code complexity.

Time Improvement of Smith-Waterman Algorithm Using OpenMP and SIMD

Abstract: Sequence alignment is a problem in bioinformatics that involves arranging sequences of proteins, RNA or DNA so that similar regions between two or more sequences may be determined. The Smith-Waterman algorithm is a key algorithm for aligning sequences. This paper uses the OpenMP application-programming interface along with the Single-Instruction Multiple-Data (SIMD) instructions. Advanced Vector Instructions 2 (AVX2) is used to implement the SIMD paradigm. It utilizes both fine-level and coarse-level parallelism to improve resource utilization without requiring support from multiple nodes in a distributed memory system. The algorithm shows a multifold decrease in execution time in comparison to an implementation that is sequentially executed.

Software Maintainability: Systematic Literature Review and Current Trends

Abstract: Software maintenance is an expensive activity that consumes a major portion of the cost of the total project Various activities carried out during maintenance include the addition of new features, deletion of obsolete code, correction of errors, etc Software maintainability means the ease with which these operations can be carried out If the maintainability can be measured in early phases of the software development, it helps in better planning and optimum resource utilization Measurement of design properties such as coupling, cohesion, etc in early phases of development often leads us to derive the corresponding maintainability with the help of prediction models In this paper, we performed a systematic review of the existing studies related to software maintainability from January 1991 to October 2015 In total, 96 primary studies were identified out of which 47 studies were from journals, 36 from conference proceedings and 13 from others All studies were compiled in structured form and analyzed through numerous perspectives such as the use of design metrics, prediction model, tools, data sources, prediction accuracy, etc According to the review results, we found that the use of machine learning algorithms in predicting maintainability has increased since 2005 The use of evolutionary algorithms has also begun in related sub-fields since 2010 We have observed that design metrics is still the most favored option to capture the characteristics of any given software before deploying it further in prediction model for determining the corresponding software maintainability A significant increase in the use of public dataset for making the prediction models has also been observed and in this regard two public datasets User Interface Management System (UIMS) and Quality Evaluation System (QUES) proposed by Li and Henry is quite popular among researchers Although machine learning algorithms are still the most popular methods, however, we suggest that researchers working on software maintainability area should experiment on the use of open source datasets with hybrid algorithms In this regard, more empirical studies are also required to be conducted on a large number of datasets so that a generalized theory could be made The current paper will be beneficial for practitioners, researchers and developers as they can use these models and metrics for creating benchmark and standards Findings of this extensive review would also be useful for novices in the field of software maintainability as it not only provides explicit definitions, but also lays a foundation for further research by providing a quick link to all important studies in the said field Finally, this study also compiles current trends, emerging sub-fields and identifies various opportunities of future research in the field of software maintainability

Data Mining in Healthcare

Abstract: The data mining has played a significant role in the healthcare industry because of its descriptive and predictive power It has been used in predicting various types of diseases The data mining helps in planning healthcare activities and reducing the number of inpatients in the hospital It can also be used for decision-making at different levels of the healthcare sector This paper provides a brief introduction to data mining and its applications in the healthcare industry

The impact of feature selection on maintainability prediction of service-oriented applications

Abstract: Service-oriented development methodologies are very often considered for distributed system development. The quality of service-oriented computing can be best assessed by the use of software metrics that are considered to design the prediction model. Feature selection technique is a process of selecting a subset of features that may lead to build improved prediction models. Feature selection techniques can be broadly classified into two subclasses such as feature ranking and feature subset selection technique. In this study, eight different types of feature ranking and four different types of feature subset selection techniques have been considered for improving the performance of a prediction model focusing on maintainability criterion. The performance of these feature selection techniques is evaluated using support vector machine with different types of kernels over a case study, i.e., five different versions of eBay Web service. The performances are measured using accuracy and F-measure value. The results show that maintainability of the service-oriented computing paradigm can be predicted by using object-oriented metrics. The results also show that it is possible to find a small subset of object-oriented metrics which helps to predict maintainability with higher accuracy and also reduces the value of misclassification errors.

A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Abstract: Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.