J. Ryder

Bio: J. Ryder is an academic researcher from Kean University. The author has contributed to research in topics: Fuzzy set operations & Sensitivity analysis. The author has an hindex of 1, co-authored 1 publications receiving 63 citations.

Fuzzy modeling of software effort prediction

Abstract: Software sizing is an important management activity for both customer and developer that is characterized by uncertainty. Fuzzy system modeling offers a means to capture and logically reason with uncertainty. The paper investigates the application of fuzzy modeling techniques to two of the most widely used software effort prediction models: the Constructive Cost Model and the Function Points model.

Software engineering economics

Abstract: 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.

Adaptive fuzzy logic-based framework for software development effort prediction

Abstract: Algorithmic effort prediction models are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. In this paper, we present an adaptive fuzzy logic framework for software effort prediction. The training and adaptation algorithms implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, offers transparency in the prediction system, and could adapt to new environments as new data becomes available. Our validation experiment was carried out on artificial datasets as well as the COCOMO public database. We also present an experimental validation of the training procedure employed in the framework.

An Improved Fuzzy Approach for COCOMO’s Effort Estimation Using Gaussian Membership Function

Abstract: In software industry Constructive Cost Model (COCOMO) is considered to be the most widely used model for effort estimation. Cost drivers have significant influence on the COCOMO and this research investigates the role of cost drivers in improving the precision of effort estimation. It is important to stress that uncertainty at the input level of the COCOMO yields uncertainty at the output, which leads to gross estimation error in the effort estimation. Fuzzy logic has been applied to the COCOMO using the symmetrical triangles and trapezoidal membership functions to represent the cost drivers. Using Trapezoidal Membership Function (TMF), a few attributes are assigned the maximum degree of compatibility when they should be assigned lower degrees. To overcome the above limitation, in this paper, it is proposed to use Gaussian Membership Function (GMF) for the cost drivers by studying the behavior of COCOMO cost drivers. The present work is based on COCOMO dataset and the experimental part of the study illustrates the approach and compares it with the standard version of the COCOMO. It has been found that Gaussian function is performing better than the trapezoidal function, as it demonstrates a smoother transition in its intervals, and the achieved results were closer to the actual effort.

Research patterns and trends in software effort estimation

Abstract: Context Software effort estimation (SEE) is most crucial activity in the field of software engineering. Vast research has been conducted in SEE resulting into a tremendous increase in literature. Thus it is of utmost importance to identify the core research areas and trends in SEE which may lead the researchers to understand and discern the research patterns in large literature dataset. Objective To identify unobserved research patterns through natural language processing from a large set of research articles on SEE published during the period 1996 to 2016. Method A generative statistical method, called Latent Dirichlet Allocation (LDA), applied on a literature dataset of 1178 articles published on SEE. Results As many as twelve core research areas and sixty research trends have been revealed; and the identified research trends have been semantically mapped to associate core research areas. Conclusions This study summarises the research trends in SEE based upon a corpus of 1178 articles. The patterns and trends identified through this research can help in finding the potential research areas.

A soft computing framework for software effort estimation

Abstract: Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. In this paper, we present a soft computing framework to tackle this challenging problem. We first use a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. One feature of this framework is that the architecture is inherently independent of the choice of algorithmic models or the nature of the estimation problems. By integrating neural networks, fuzzy logic and algorithmic models into one scheme, this framework has learning ability, integration capability of both expert knowledge and project data, good interpretability, and robustness to imprecise and uncertain inputs. Validation using industry project data shows that the framework produces good results when used to predict software cost.