Object-Oriented Software Metrics.

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

We have seen a spate of books dealing with the inevitable confusions and frustrations of a society in which institutions proliferate at a rate far faster than any faint improvement in the moral or intellectual capacities of man. We have been told about these in "The Managerial Revolution" and "The Organization Man." Medical people long familiar with Parkinsonism, Parkinson's disease, or Parkinson's syndrome will be pleased to learn of the recent discovery and mathematical demonstration of Parkinson's law. In any event, a citizen purporting to be one C. Northcote Parkinson, "Raffles Professor of History at the University of Malaya," defines briefly and in Parkinson's own and splendid style, Parkinson's law. It holds that in any administrative organization, administrators multiply at a rate of increase of approximately six per cent per annum no matter what. His derivation of the formula shows mathematical genius of a kind never quite achieved by Einstein.

Parkinson's Law, and Other Studies in Administration.

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call regression fuzzy logic. State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

Software Development Effort Estimation Using Regression Fuzzy Models

The purpose of this paper is to examine the factors responsible for growing popularity of digital wallets in India and sustainibility challenges faced by this innovative product on account of gaps between expectations of the users and their satisfaction level with leading wallet brands like Paytm, Freecharge, Mobikwik and Oxigen.,The descriptive research is based on primary data collected with the help of a structured questionnaire from 313 respondents in National Capital Region of Delhi chosen through non-probability convenience sampling. The collected data were converted into data matrix using SPSS 23.0 software and inferential analysis was done.,Attractive cashback and rewards, ease of use, instant money transfer without using cash, relatively higher transaction security as compared to credit/debit cards and absence of any transaction fee are the factors responsible for growing use of digital wallets. However, there are gaps between customers’ expectations and the satisfaction level which pose a challenge for sustainibility of digital wallets.,The study is limited to National Capital Region of Delhi for a specified set of factors considered important for customers’ satisfaction.,This paper offers fresh insights into the gaps between Indian customers’ expectations their satisfaction level with the leading digital wallet brands operating in India, which can be used to bridge these gaps for ensuring their long-term sustainability in a competitive environment.

Gaps in customer satisfaction with digital wallets: challenge for sustainability

An efficient Devanagari character classification in printed and handwritten documents using SVM

In the current era, there is a high demand of accurate text identification and categorization methods in N - Lingual non-scanned and scanned machine printed documents, where N represents mono, bi, tri or multi mode. In this paper, a technical study and analysis is presented to show N-lingual document classification for normal text, printed and handwritten documents. Text classification for normal text documents is simple, whereas in scanned machine printed systems, it inherently begins with the correct recognition of text, i.e.; characters and words. The steps involved in the latter case are script identification, page layout determination, separation of text and non-text data, line segmentation, word detection and finally character recognition. After performing such processing steps, text or script is identified and separated. Three statistically analyzed charts are also shown, which are based on content type classification, language-mode pair and most-to-least preferred languages of existing algorithms.

A technical study and analysis of text classification techniques in N - Lingual documents

In recent years, many information retrieval, character recognition, and feature extraction methodologies in Devanagari and especially in Hindi have been proposed for different domain areas. Due to enormous scanned data availability and to provide an advanced improvement of existing Hindi automated systems beyond optical character recognition, a new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic is introduced. This first pre-processes and then classifies textual imaged documents into predefined categories. With this concept, this article depicts a feasibility study of such systems with the relevance of Hindi, a survey report of statistical measurements of Hindi keywords obtained from different sources, and the inherent challenges found in printed and handwritten documents. The technical reviews are provided and graphically represented to compare many parameters and estimate contents, forms and classifiers used in various existing techniques.

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

In today’s world, several digitized Hindi text documents are generated daily at the Government sites, news portals, and public and private sectors, which are required to be classified effectively into various mutually exclusive pre-defined categories. As such, many Hindi text-based processing systems exist in application domains of information retrieval, machine translation, text summarization, simplification, keyword extraction, and other related parsing and linguistic perspectives, but still, there is a wide scope to classify the extracted text of Hindi documents into pre-defined categories using a classifier. In this paper, a Hindi Text Classification model is proposed, which accepts a set of known Hindi documents, preprocesses them at document, sentence and word levels, extracts features, and trains SVM classifier, which further classifies a set of Hindi unknown documents. Such text classification becomes challenging in Hindi due to its large set of available conjuncts and letter combinations, its sentence structure, and multisense words. The experiments have been performed on a set of four Hindi documents of two categories, which have been classified by SVM with 100% accuracy.

An Efficient Hindi Text Classification Model Using SVM

A successful project is one that is delivered on time, within budget and with the required quality. Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. A number of estimation models exist for effort prediction. However, there is a need for novel model to obtain more accurate estimations. As Artificial Neural Networks (ANN's) are universal approximators, Neuro-fuzzy system is able to approximate the non-linear function with more precision by formulating the relationship based on its training. In this paper we explore Neuro-fuzzy techniques to design a suitable model to utilize improved estimation of software effort for NASA software projects. Comparative Analysis between Neuro-fuzzy model and the traditional software model(s) such as Halstead, WalstonFelix, Bailey-Basili and Doty models is provided. The evaluation criteria are based upon MMRE (Mean Magnitude of Relative Error) and RMSE (Root mean Square Error). Integration of neural networks, fuzzy logic and algorithmic models into one scheme has resulted in providing robustness to imprecise and uncertain inputs.

Satya Prakash Singh

Papers

An efficient Devanagari character classification in printed and handwritten documents using SVM

A technical study and analysis of text classification techniques in N - Lingual documents

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

An Efficient Hindi Text Classification Model Using SVM

Software effort estimation using Neuro-fuzzy approach