A value added predictive defect type distribution model

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distribution model of software quality Assurance and management . Reference is https://ieeexplore.ieee.org/document/4529863/

Prediction of Defect based on Project Characteristics
in Software
UMESHCHANDRAYADAV
ROLL NO.1783910909
III year CSE
Department of Computer Science & Engineering
RAJKIYA ENGINEERING COLLEGE ,KANNAUJ

Abstract
A defect is an error in coding or logic.
In software project management
1. Size
2. Effort
3. Quality
In present time does not have a complete defect prediction for a software
product although much work has been performed to predict software
quality.
The number of defects cannot be sufficient information to provide the basis
for planning quality assurance activities and assessing them during
execution.

Introduction
In this paper predicting the distribution of defects and their types based on
project characteristics and the model for prediction using the curve-
fitting method ,regression analysis and Weibull probability density
function in maximum likelihood estimation (MLE).
Mostly in software engineering community has been to develop useful
models that can the software development life-cycle and accurately
predict the cost, schedule and quality of developing a software product.
This approach can support to plan suitable quality assurance activities
and prevent possible defects.
It can also help us to reduce the efforts of performing reworks and the cost
of producing high quality software.

Approach of the Proposed Model
In this paper aimed to predict the distribution of inprocess defects, their types
to be detected in the software project.
1) Analysis of literature
2) Behavioral analysis
3) Data gathering
4) Statistical modeling
5) Regression analysis
6) Model validation
7) Gathering of more data for
refining the model in the future.

A value added predictive defect type distribution model

• Defect Data
It’s obtained at all phases in the
development life cycle
(requirement analysis, design,
coding, and testing)

• Project Characteristics
In this model use 18 fully
develop software most 11
factor choose for characteristics.

• Model Construction
Statistical Modelling
Using the actual project data, curve fitting is performed to extract the
parameters of the Weibull distribution function for each project.
Regression Analysis
We identify the relationship between historical project characteristics
and the parameters of defect distribution from the statistical modelling.

• Proposed Model
K is total defect density
i is phase sequence
j is type of defect
α and β are shape parameter

• Validation
Evaluation Criteria
Predictions PRED(X)
Magnitude of relative error (MRE)
• Results of Total Defect Distributions

• Results of Defect Type Distributions
PRED(30) from 50% to 94% in requirement analysis phase,
PRED(30) from 56% to 94% in the design phase,
PRED(30) from 44% to 97% in the coding phase,
PRED(30) from 33% to 94% in the testing phase.
Thus overall PRED(30) of 75% on
average for all phases and
all defect types.

• Final Output
The distributions of actual defect data, estimated data by MLE, and predicted
data by the proposed model for a project in order to
compare the results

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A value added predictive defect type distribution model

1. Prediction of Defect based on Project Characteristics in Software UMESHCHANDRAYADAV ROLL NO.1783910909 III year CSE Department of Computer Science & Engineering RAJKIYA ENGINEERING COLLEGE ,KANNAUJ

2. Abstract A defect is an error in coding or logic. In software project management 1. Size 2. Effort 3. Quality In present time does not have a complete defect prediction for a software product although much work has been performed to predict software quality. The number of defects cannot be sufficient information to provide the basis for planning quality assurance activities and assessing them during execution.

3. Introduction In this paper predicting the distribution of defects and their types based on project characteristics and the model for prediction using the curve- fitting method ,regression analysis and Weibull probability density function in maximum likelihood estimation (MLE). Mostly in software engineering community has been to develop useful models that can the software development life-cycle and accurately predict the cost, schedule and quality of developing a software product. This approach can support to plan suitable quality assurance activities and prevent possible defects. It can also help us to reduce the efforts of performing reworks and the cost of producing high quality software.

4. Approach of the Proposed Model In this paper aimed to predict the distribution of inprocess defects, their types to be detected in the software project. 1) Analysis of literature 2) Behavioral analysis 3) Data gathering 4) Statistical modeling 5) Regression analysis 6) Model validation 7) Gathering of more data for refining the model in the future.

6. • Defect Data It’s obtained at all phases in the development life cycle (requirement analysis, design, coding, and testing)

7. • Project Characteristics In this model use 18 fully develop software most 11 factor choose for characteristics.

8. • Model Construction Statistical Modelling Using the actual project data, curve fitting is performed to extract the parameters of the Weibull distribution function for each project. Regression Analysis We identify the relationship between historical project characteristics and the parameters of defect distribution from the statistical modelling.

9. • Proposed Model K is total defect density i is phase sequence j is type of defect α and β are shape parameter

10. • Validation Evaluation Criteria Predictions PRED(X) Magnitude of relative error (MRE) • Results of Total Defect Distributions

11. • Results of Defect Type Distributions PRED(30) from 50% to 94% in requirement analysis phase, PRED(30) from 56% to 94% in the design phase, PRED(30) from 44% to 97% in the coding phase, PRED(30) from 33% to 94% in the testing phase. Thus overall PRED(30) of 75% on average for all phases and all defect types.

12. • Final Output The distributions of actual defect data, estimated data by MLE, and predicted data by the proposed model for a project in order to compare the results

13. Thank You

A value added predictive defect type distribution model

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