Tracking mistakes in software measurement using fuzzy data analysis
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TRACKING MISTAKES IN SOFTWARE MEASUREMENT USING FUZZY DATA ANALYSIS. Militon FRENŢIU, Horia F. POP Department of Computer Science Babeş-Bolyai University. Contents. Introduction Experimental study Software metrics Fuzzy PCA, Fuzzy Hierarchic Clustering Results Comments Conclusions.

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TRACKING MISTAKES IN SOFTWARE MEASUREMENT USING FUZZY DATA ANALYSIS

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Tracking mistakes in software measurement using fuzzy data analysis

TRACKING MISTAKES IN SOFTWARE MEASUREMENTUSING FUZZY DATA ANALYSIS

Militon FRENŢIU, Horia F. POP

Department of Computer Science

Babeş-Bolyai University


Contents

Contents

  • Introduction

  • Experimental study

  • Software metrics

  • Fuzzy PCA, Fuzzy Hierarchic Clustering

  • Results

  • Comments

  • Conclusions


Introduction

Introduction

  • Software metrics

    • improve the software development process

    • evaluate the quality of a software product

    • useful in education

  • Issues approached by us

    • effect of programming style on software attributes

    • study of relationships between attributes

    • effect of education on projects quality


Experimental study

Experimental study

  • 34 projects produced by second year undergraduate students as part of their requirements curriculum

  • attributes evaluated by postgraduate students from a Master group studying Software metrics

  • use of fuzzy data analysis methods for tracking mistakes in projects evaluation


Software metrics

requirements description

good specification

function points

clarity of design

correctness of design

completeness of design

diagrams of design

modules specification

algorithms description

lines of code

no. of comments

good use of comments

good use of free lines

indentation

good names

readability

comprehensibility

number of classes

number of methods for all classes

changeability (modifiability)

structuredness

testability

reliability

efficiency

extensibility

adaptability

clarity of documentation

maintainability

simplicity

usability

portability

quality

average of weighted methods per class

depth of inheritance

Software metrics


Fuzzy pca

Fuzzy PCA

  • Fuzzy Principal Components Analysis

  • Data dimensionality reduction: new, fewer variables

  • Principal components correspond to axes of maximal elongation of data

  • Principal components necessary to conserve 90% of data variance is considerably less than the size of data space

  • Advantage: fuzziness in data, robustness, isolated points


Fuzzy clustering

Fuzzy Clustering

  • Data representation: vectors of measured values

  • Feature extraction: to reduce data dimensionality and eliminateredundant characteristics

  • Clusters shape: different geometric prototypes; norms or scalar products

  • Clusters size: use of adaptive distance or adaptive algorithms

  • Clusters validity: optimal number of classes through validity functionals,clusters merging/splitting or by using a hierarchical approach

  • Final fuzzy partition: needs to be defuzzied; it should not be discarded

  • Method: fuzzy objective function minimization


Results fpca

Results: FPCA


Results fuzzy clustering

Results: Fuzzy Clustering

  • ClassMembers(Fuzzy membership

  • 1.1.13 5 8 14 27 30 degrees are essential

  • 1.1.27 29 31 33 to understand results)

  • 1.2.1.11 10 16 19

  • 1.2.1.26 9 15 21 22 25 26 32

  • 1.2.2.12 11 17

  • 1.2.2.24 13 18 23

  • 2.128

  • 2.2.1.120

  • 2.2.1.212

  • 2.2.224


Comments and conclusions

Comments and conclusions

  • Isolated (individual or grouped) points

  • Meaning: different mistakes in measures

  • P27 – incorrect data typing (classes – comments)

  • P30 – incorrect typing (comments - lines)

  • P20 – relationship (wrong changeability and structuredness)

  • P12 – relationship (wrong comprehensibility)

  • P24 – relationship (wrong changeability)

  • P28 – relationship (wrong comprehensibility)


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