Author
Xishi Huang
Bio: Xishi Huang is an academic researcher from University of Western Ontario. The author has contributed to research in topics: COCOMO & Software. The author has an hindex of 4, co-authored 6 publications receiving 293 citations.
Topics: COCOMO, Software, Interpretability, Neuro-fuzzy, Soft computing
Papers
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01 Jan 2007
TL;DR: A novel neuro-fuzzy Constructive Cost Model (COCOMO) is proposed for software cost estimation that allows input to have continuous rating values and linguistic values, thus avoiding the problem of similar projects having large different estimated costs.
Abstract: Accurate software development cost estimation is important for effective project management such as budgeting, project planning and control. So far, no model has proved to be successful at effectively and consistently predicting software development cost. A novel neuro-fuzzy Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of a neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, the proposed model can be interpreted and validated by experts, and has good generalization capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. In addition, it allows input to have continuous rating values and linguistic values, thus avoiding the problem of similar projects having large different estimated costs. A detailed learning algorithm is also presented in this work. The validation using industry project data shows that the model greatly improves estimation accuracy in comparison with the well-known COCOMO model.
142 citations
15 Jan 2006
TL;DR: This paper uses a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals, and proposes a default algorithmic model that can be replaced when a better model is available.
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.
62 citations
06 Nov 2003
TL;DR: A novel neuro-fuzzy constructive cost model (COCOMO) for software estimation that allows inputs to be continuous-rating values and linguistic values, therefore avoiding the problem of similar projects having different estimated costs.
Abstract: A novel neuro-fuzzy constructive cost model (COCOMO) for software estimation is proposed. The model carries some of the desirable features of the neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, this model is easily validated by experts and capable of generalization. In addition, it allows inputs to be continuous-rating values and linguistic values, therefore avoiding the problem of similar projects having different estimated costs. Also presented in this paper is a detailed learning algorithm. The validation, using industry project data, shows that the model greatly improves the estimation accuracy in comparison with the well-known COCOMO model.
50 citations
Patent•
18 Aug 2004
TL;DR: In this article, a pre-processing neuro-fuzzy inference system is used to resolve the effect of dependencies among contributing factors to produce adjusted rating values for the contributing factors.
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.
43 citations
Posted Content•
TL;DR: An intelligent approach to software cost prediction by integrating the neuro-fuzzy technique with the well-accepted COCOMO model that can make the best use of both expert knowledge and historical project data is presented.
Abstract: Good software cost prediction is important for effective project management such as budgeting, project planning and control. In this paper, we present an intelligent approach to software cost prediction. By integrating the neuro-fuzzy technique with the well-accepted COCOMO model, our approach can make the best use of both expert knowledge and historical project data. Its major advantages include learning ability, good interpretability, and robustness to imprecise and uncertain inputs. The validation using industry project data shows that the model greatly improves prediction accuracy in comparison with the COCOMO model.
4 citations
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01 Apr 1998
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586 citations
TL;DR: A systematic literature review of empirical studies on ML model published in the last two decades finds that eight types of ML techniques have been employed in SDEE models, and overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models.
Abstract: Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Objective: This research aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. Method: We performed a systematic literature review of empirical studies on ML model published in the last two decades (1991-2010). Results: We have identified 84 primary studies relevant to the objective of this research. After investigating these studies, we found that eight types of ML techniques have been employed in SDEE models. Overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models. Furthermore, different ML models have different strengths and weaknesses and thus favor different estimation contexts. Conclusion: ML models are promising in the field of SDEE. However, the application of ML models in industry is still limited, so that more effort and incentives are needed to facilitate the application of ML models. To this end, based on the findings of this review, we provide recommendations for researchers as well as guidelines for practitioners.
403 citations
01 Jan 1981
TL;DR: In this article, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
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.
283 citations
TL;DR: This paper proposes the project selection technique for ABE (PSABE) which reduces the whole project base into a small subset that consist only of representative projects and shows promising results that could significantly improve analogy based models for software cost estimation.
197 citations
TL;DR: A novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams is presented and demonstrates that the MLP model can surpass the regression model when small projects are used, but the log- linear regression model gives better results when estimating larger projects.
192 citations