J.M. Calvert

Bio: J.M. Calvert is an academic researcher from University of Otago. The author has contributed to research in topics: Application software & Software. The author has an hindex of 2, co-authored 2 publications receiving 56 citations.

FULSOME: fuzzy logic for software metric practitioners and researchers

Abstract: There has been increasing interest in recent times for using fuzzy logic techniques to represent software metric models, especially those predicting the software development effort. The use of fuzzy logic for this application area offers several advantages when compared to other commonly-used techniques. These include the use of a single model with different levels of precision for the inputs and outputs used throughout the development life-cycle, the possibility of model development with little or no data, and its effectiveness when used as a communication tool. The use of fuzzy logic in any applied field, however, requires that suitable tools are available for both practitioners and researchers-satisfying both interface- and functionality-related requirements. After outlining some of the specific needs of the software metrics community, including results from a survey of software developers on this topic, this paper describes the use of a set of tools called FULSOME (FUzzy Logic for SOftware MEtrics). The development of a simple fuzzy logic system by a software metrician and its subsequent tuning are then discussed using a real-world set of software metric data. The automatically generated fuzzy model performs acceptably when compared to regression-based models.

FULSOME: a fuzzy logic modeling tool for software metricians

Abstract: There has been a growing body of literature suggesting that some of the problems faced by software development project managers can be at least partially overcome by using fuzzy logic techniques. However, one issue that has been generally overlooked in this recommendation is the means by which these "software metricians" can collect data for, develop, and interpret fuzzy logic models in practice. We describe a freely available system that has been built with this in mind called FULSOME (FUzzy Logic for SOftware MEtrics). While there are many tools available for developing fuzzy models, it is suggested that before there will be real adoption of such techniques by project managers there will need to be suitable tools that support their particular workflows and that use appropriate terminology. Another requirement will be the development of some standard procedures and definitions for such models. Issues involved with membership function elicitation and extraction are also discussed as a first step towards this second goal.

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.

Computational intelligence as an emerging paradigm of software engineering

Abstract: Software Engineering is inherently knowledge intensive. Software processes and products are human centered. The technology of Computational Intelligence (CI) intensively exploits various mechanisms of interaction with humans and processes domain knowledge with intent of building intelligent systems. As commonly perceived, CI dwells on three highly synergistic technologies of neural networks, fuzzy sets (or granular computing, in general) and evolutionary optimization. As the software complexity grows and the diversity of software systems skyrocket, it becomes apparent that there is a genuine need for a solid, efficient, designer-oriented vehicle to support software analysis, design, and implementation at various levels. The research agenda makes CI a highly compatible and appealing vehicle to address the needs of knowledge rich environment of Software Engineering. The objective of this study is to identify and discuss synergistic links emerging between Software Engineering and Computational Intelligence. We show how CI --- based models contribute to the methodology of constructing models of software processes and products. Several selected examples (including software cost estimation, quality, and software measures) are included.

Software source code sizing using fuzzy logic modeling

Abstract: Knowing the likely size of a software product before it has been constructed is potentially beneficial in project management: for instance, size can be an important factor in determining an appropriate development/integration schedule, and it can be a significant input in terms of the allocation of personnel and other resources. In this study we consider the applicability of fuzzy logic modeling methods to the task of software source code sizing, using a previously published data set. Our results suggest that, particularly with refinement using data and knowledge, fuzzy predictive models can outperform their traditional regression-based counterparts.

System and method for software estimation

Abstract: A system and method for software estimation. In one embodiment, the software estimation system comprises a pre-processing neuro-fuzzy inference system used to resolve the effect of dependencies among contributing factors to produce adjusted rating values for the contributing factors, a neuro-fuzzy bank used to calibrate the contributing factors by mapping the adjusted rating values for the contributing factors to generate corresponding numerical parameter values, and a module that applies an algorithmic model (e.g. COCOMO) to produce one or more software output metrics.

Software Development Effort Estimation Using Fuzzy Logic - A Survey

Abstract: Most of the software estimates should be performed at the beginning of the life cycle, when we do not yet know the problem we are going to solve. Effort estimation is used to predict how many hours of work and how many workers are needed to develop a project. The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. Estimating the effort with a high grade of reliability is a problem which has not yet been solved and even the project manager has to deal with it since the beginning. Fuzzy systems try to emulate cognitive processes of the brain with a rule base. The basic concept is inspired by the human processes where the decisional criteria are not clear cut, but blurred and it is difficult to find objective to make the decisions more precise and clear. Fuzzy decision systems are based on fuzzy logic that tries to reproduce the fuzzy human reasoning. The aim of this survey is to analyze the use of fuzzy logic in the existing models and to provide in depth review of software and project estimation techniques existing in industry and literature, its strengths and weaknesses.