### Abstract

Original language | Undefined |
---|---|

Pages (from-to) | 57-80 |

Number of pages | 24 |

Journal | Fuzzy sets and systems |

Volume | 145 |

Issue number | 145 |

DOIs | |

Publication status | Published - 1 Jul 2004 |

### Keywords

- METIS-220446
- EWI-10135
- IR-48727
- Object-oriented methods
- Adaptable design models
- Fuzzy inference systems
- Approximate reasoning
- Quantization error

### Cite this

}

*Fuzzy sets and systems*, vol. 145, no. 145, pp. 57-80. https://doi.org/10.1016/j.fss.2003.10.005

**Fuzzy logic-based object-oriented methods to reduce quantization error and contextual bias problems in software development.** / Marcelloni, Francesco; Aksit, Mehmet.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Fuzzy logic-based object-oriented methods to reduce quantization error and contextual bias problems in software development

AU - Marcelloni, Francesco

AU - Aksit, Mehmet

PY - 2004/7/1

Y1 - 2004/7/1

N2 - During the last several years, a considerable number of software development methods have been introduced to produce robust, reusable and adaptable software systems. Methods create software artifacts through the application of a large number of heuristic rules. These rules are generally expressed in two-valued logic. In object-oriented methods, for instance, candidate classes are identified by applying the following intuitive rule: If an entity in a requirement specification is relevant and can exist autonomously in the application domain, then select it as a class. In this paper, we identify and define two major problems regarding how rules are defined and applied in current methods. First, two-valued logic cannot effectively express the approximate and inexact nature of a typical software development process. Although software engineers can perceive partial relevance of an entity and possibly select the entity as a partial candidate class, they are constrained by two-valued logic to quantize relevance into relevant and irrelevant. Second, the influence of contextual factors on rules is generally not modelled explicitly. We term these problems as quantization error and contextual bias problems, respectively. To reduce these problems, we propose to express heuristic rules using fuzzy logic. We illustrate formally how fuzzy logic-based methodological rules can help in lowering the effects of quantization error and contextual bias problems.

AB - During the last several years, a considerable number of software development methods have been introduced to produce robust, reusable and adaptable software systems. Methods create software artifacts through the application of a large number of heuristic rules. These rules are generally expressed in two-valued logic. In object-oriented methods, for instance, candidate classes are identified by applying the following intuitive rule: If an entity in a requirement specification is relevant and can exist autonomously in the application domain, then select it as a class. In this paper, we identify and define two major problems regarding how rules are defined and applied in current methods. First, two-valued logic cannot effectively express the approximate and inexact nature of a typical software development process. Although software engineers can perceive partial relevance of an entity and possibly select the entity as a partial candidate class, they are constrained by two-valued logic to quantize relevance into relevant and irrelevant. Second, the influence of contextual factors on rules is generally not modelled explicitly. We term these problems as quantization error and contextual bias problems, respectively. To reduce these problems, we propose to express heuristic rules using fuzzy logic. We illustrate formally how fuzzy logic-based methodological rules can help in lowering the effects of quantization error and contextual bias problems.

KW - METIS-220446

KW - EWI-10135

KW - IR-48727

KW - Object-oriented methods

KW - Adaptable design models

KW - Fuzzy inference systems

KW - Approximate reasoning

KW - Quantization error

U2 - 10.1016/j.fss.2003.10.005

DO - 10.1016/j.fss.2003.10.005

M3 - Article

VL - 145

SP - 57

EP - 80

JO - Fuzzy sets and systems

JF - Fuzzy sets and systems

SN - 0165-0114

IS - 145

ER -