Pradeep Kumar Bhatia

A Detailed Review of Feature Extraction in Image Processing Systems

Feature plays a very important role in the area of image processing. Before getting features, various image preprocessing techniques like binarization, thresholding, resizing, normalization etc. are applied on the sampled image. After that, feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. Here in this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique, will be better. Hereby in this paper, we are going to refer features and feature extraction methods in case of character recognition application.

Predicting software maintenance using fuzzy model

Software maintenance is commonly used to refer to the modifications that are made to a software system after its initial release, installed and is operational. There is evidence that maintenance costs exceed 60 percent of the total costs of software. In this paper we have analyzed the major factors that can affect software maintenance and divide them into four categories: Readability of Source Code (RSC), Documentation Quality (DQ), Understandability of Software (UOS), and Average Cyclomatic Complexity (ACC). In our study we have proposed fuzzy model to predict software maintenance using these four factors. The proposed fuzzy model is validated and experimental results indicate that the proposed model is suitable for predicting software maintenance level of the software.

Comparative Analysis of Software Engineering Models from Traditional to Modern Methodologies

Software Engineering aims to produce a quality software product that is delivered on time, within the allocated budget, and with the requirements expected by the customer but unfortunately maximum of the times this goal is rarely achieved. A software life cycle is the series of identifiable stages that a software product undergoes during its lifetime. However, a properly managed project in a matured software engineering environment can consistently achieve this goal. This research is concerned with the methodologies that examine the life cycle of software through the development models, which are known as software development life cycle. Hereby, we are representing traditional i.e. waterfall, Iteration, Spiral models as well as modern development methodologies like Agile methodologies that includes Extreme programming, Scrum, Feature Driven Development, Component based software development methodologies etc. All of these models have advantages and disadvantages as well. Therefore, the main objective of this research is to represent different models of software development by showing the good and bad practices of each model. A comparative analysis of traditional as well as modern methodologies is made.

Estimation of generic reusability for object-oriented software an empirical approach

Inheritance and templates are key concepts in object-oriented programming (OOP), and are essential for achieving reusability and extendibility. The aim of this paper is to explore traditional Halsteads metrics and use them to propose more software metrics related to generic method and attributes in an object-oriented software. These metrics measure quantitative generic construct with inheritance in an object-oriented code. Two metrics GRr (Generic Reusability Ratio) and ERr (Effort Ratio) are proposed in this paper. First metric GRr (Generic Reusability Ratio) measures impact of template in program volume and second metric ERr (Effort Ratio) measures impact of template in development effort. These metrics will be a tool for estimating and evaluating costs of program design and program tests as well as program complexity.

Analysis of reusability of object-oriented systems using object-oriented metrics

In object-oriented systems, assessing reusability plays a key role in reducing a cost and improving the quality of the software. Objectoriented programming helps in achieving the concept of reusability through different types of inheritance programs, which further help in developing reusable software modules. And object-oriented metrics identify the effectiveness of each reuse strategy. Software reusability has considerable effect on software quality. Software quality increases as reuse of software components increases. But software quality improvement cannot be understood unless it is measured. This paper focuses on an empirical evaluation of object-oriented metrics in C++ using three different object-oriented features. Three programs have been considered as input for the study -- the first program uses multilevel inheritance, the second program uses multiple inheritance and the third program uses hierarchical inheritance. We have found that multilevel inheritance has more impact on reusability among these three features.

Analysis of Reusability of Object-Oriented System using CK Metrics

In the object-oriented environment, one of the most important aspects having strong influence on the quality of resulting software system is the design complexity. The OO model offers the technology to create components that can be used for general programming. Design complexity has been imagining to play a strong role in the quality of the resulting software system in OO development environments. This paper gives the design of CK suit of metrics and evaluation to these metrics so that these metrics should reflect accurate and precise results for object oriented based systems. Moreover, a set of new metrics are proposed that can find the impact on reusability of a class by using the combination of one CK metric with another metric.

ANN model for predicting software function point metric

Software Engineering measurement and analysis specially, size estimation initiatives have been in the center of attention for many firms. Function Point (FP) metric is among the most commonly used techniques to estimate the size of software system projects or software systems for measuring the functionality delivered by a system. In this paper we explore an alternative, Artificial Neural Network (ANN) approach for predicting function Point. We proposed an ANN model to explore neural network as tool for function point metric. A multilayer feed forward network is trained using backpropogation algorithm and demonstrated to be suitable. The training and validation data is randomly selected from the data repository of 365 projects [7]. The experimental results of two validation sets each of 55 projects indicate that the Mean Absolute Relative Error (MARE) was 0.198 and 0.145 of ANN model and shows that ANN model is a competitive model as Function Point Metric.

Designing metrics for caching techniques for dynamic web site

Today a major drift is observed from static website to dynamic websites. The dynamic websites deliver the customized contents to their users. These websites cover a wide spectrum of applications which largely vary in its content, configuration and volume of traffic. The popular metrics designed to evaluate a dynamic website are response time, user satisfaction, performance, scalability, usability personalization, reliability, reusability and security. But for dynamic website delivery, update, consistency maintenance are main hurdles. To overcome them and to serve and deliver the contents efficiently for dynamic website researchers have proposed several caching mechanisms with an aim to reduce the construction overhead to improve the response time as well as reusability of its contents. A great research has been performed on various aspects of caching. Based on these the caching techniques are classified depending on its location, content, replacement, replication and updation strategy and many more. A caching technique can be suitable for one website and may not be effective for other website, depending upon the configuration, content and application of website one should choose the suitable caching technique. Thus a need of designing a set of metrics on which these caching techniques can be evaluated is much required. In this paper we will study about the classification of caching techniques and then discuss the issues related to caching and various criteria on which metrics are needed to be designed to evaluate the effectiveness of caching techniques.

Investigating of high and low impact faults in object-oriented projects

For optimum utilization of resources and reducing the cost of software, the fault detection and elimination process must be properly planned and for this type of planning prediction of fault-prone module is gaining importance among researchers. It would be valuable to know how object-oriented design metrics and class fault-proneness are related when fault impact is taken into account. In this paper, we use the logistic regression method to empirically investigate the usefulness of object-oriented design metrics in predicting fault-proneness when taking fault impact into account. Our results, based on a public domain NASA Promise data set, indicate that most of these design metrics are statistically related to fault-proneness of classes across fault impact, and the prediction capabilities of the investigated metrics greatly depend on the impact of faults. More specifically, these design metrics are able to predict high/low impact faults in fault-prone classes.

Predicting Quantitative Functional Dependency Metric based upon the Interface Complexity Metric in Component based Software Systems: A New Approach

ABSTRACT One of the major issues in component based software systems structuring and quality prediction is the interdependencies of system components. This paper proposes a novel technique for determining the strength of functional coupling in component based software systems. Authors propose Strength of Functional Dependency (SFD) metric, which is based upon two new metrics Operational Coupling complexity Index (OCI), and Instance Coupling complexity Index (ICI). It allows us to quantify the functional dependencies, formed by different kinds of operations and instances between these components. Compared to other existing dependency metrics, which are often based on number of operations or instance variables between the components only, authors consider operational complexity and instance variables complexity as a measure to how strong this dependency is and therefore promote a more systematic approach to the reasoning about modularity in component based software systems. This paper can be divided broadly into two parts. The first part quantifies interface operations and instance variables. The quantification is performed by considering the number of input, output parameters and their types. Based upon these factors of operations and instance variables, authors used analytical hierarchy approach (AHP) to assign weights to these factors and outcomes OCI, ICI and SFD. The second part shows the experimentation and validation of the proposed metrics. The advantages of the proposed method are discussed as well through a case study in this paper.