8/3/2019 SympAB

1/33

Effort Estimation in IterativeDevelopment:

Learning using Bayesian Networks

Abou Bakar Nauman

(PhD Scholar CIIT, Islamabad)Assistant Professor

Sarhad University of Science and IT, Peshawar, Pakistan

8/3/2019 SympAB

2/33

Outline Introduction to Bayesian Networks

Existing models in software engineering Research designs

Software effort estimation

Proposed model

8/3/2019 SympAB

3/33

Bayesian Network Bayesian Inference

Inference of probability distribution based

on Likelihood The process to learn the posterior

probability of a variable is inference

Bayesian Network A network containing probability distribution

of nodes, the posterior probability iscalculated based on Bayesian inference.

X2

X5X4

Xn

X1

X3

Xq

8/3/2019 SympAB

4/33

Inference Mechanism Bucket Elimination

We start with a set V of tables, and whenever we wish tomarginalize a variable X, we take from V all tables with Xin their domains. Calculate the product of them,marginalize X out of it, and place the resulting table in V.

A

B Y|X

X

8/3/2019 SympAB

5/33

Inference algorithms Joint Tree Algorithm

A large tree is developed

Joint distribution calculated based on Bucket elimination

Frontier Algorithm JPD of first time slice t0

Then children of t1 are added

Parent t0 is marginalized

Forward-Backward Pass

Boyen-Koller Algorithm

8/3/2019 SympAB

6/33

Learning

Categories and

Techniques

Data

Observable Partial

Structure

Known Maximum Likelihood

Log likelihood

Gaussian approximation

MAP.

EM (expectation

maximization)

Gradient ascent.

Unknown Heuristic Search, greedysearch, Monte Carlo

methods,

Best first search

Scoring

Search + EMHidden nodes.

Marginal likelihood, local criteria, relative posterior

probability

8/3/2019 SympAB

7/33

Parameter Learning Multiple Evidences

Batch learningAdaptation

Temporal Data

Kalman Filter Markov Models

Hidden Markov Models

Dynamic Bayesian Networks

8/3/2019 SympAB

8/33

Software Project

Estimation and Plan Intangible factors

Virtual product Complexity

Estimate >>> Plan

Assessment >>> Tracking Re Estimate >>>> New Plan

Uncertainty >>> Wrong Plan

8/3/2019 SympAB

9/33

Iterative and Incremental

development

Mini Projects

Interdependence Multiple work flows

Project planning after each iteration

Productivity

8/3/2019 SympAB

10/33

Curve of Uncertainty The Ideal situation

8/3/2019 SympAB

11/33

Problem Identification

8/3/2019 SympAB

12/33

Software Effort Estimation Multiple Process Cycles

Uncertainty Historical data

Organizational Data

Expert Opinion Multiple Evidences

Project Related Data

8/3/2019 SympAB

13/33

Research Question

H

ow can we successfully apply theBBN approach to learn and estimatedevelopment effort in softwareprojects that follow an Iterative and

Incremental approach?

8/3/2019 SympAB

14/33

Research Objectives Learning team productivity from completed iterations and using the

learned information to predict for future can improve effortestimation in Iterative and Incremental development software

development project. Bayesian Networks Provides the practical technique to estimate

effort, incorporate both uncertainty and learning from pastexperience.

Bayesian networks can be designed for effort estimation in Iterative

software development using knowledge of the causal relationshipsbetween attributes.

8/3/2019 SympAB

15/33

Research Method Structural Development

To identify the factors and develop a structural graph to showthe dependencies among different factors.

Parameter Estimation This step represents the quantitative component of a BN,

which results in conditional probabilities,

obtained via Expert Elicitation or automatically, which

quantify the relationships between variables

Model Validation Test the real data of projects

Implement in real time projects

8/3/2019 SympAB

16/33

Model Evolution Not one model

A series of models Each model is enhancement of earlier

Different features of BN explored

Variety of data M1, M2, M3, M4, M5, M6, MMMI

8/3/2019 SympAB

17/33

Proposed Modeling Approach

Sr # Title Objectives

1 Composition Identification of units of proposed model to be used in IID effort

estimation model development.

2 Structuring How different units of the model can be joined together.

3 Project Level Estimate How project level estimates can be collected before start of

project.

4 Evidence Recording How evidence of latest actual effort can be submitted in model

5 Results Interpretation How results from the model can be interpreted.

6 Results forwarding How results from one time slice can be forwarded to next slices.

7 Unrolling The procedure to use multiple instances of basic model.

8 Learning Options to control the degree of learning from latest evidences.

9 Project Scenarios To explore how model behaves in different scenarios.

10 Validation To analyze capability of model to work in industrial project.

8/3/2019 SympAB

18/33

M1: A fuzzy logic and

Classification Model

8/3/2019 SympAB

19/33

Proposed Model: M2

8/3/2019 SympAB

20/33

ISBSG data set, Productivity

120100806040200

Observed Value

120

100

80

60

40

20

0

ExpectedGammaValue

Gamma Q-Q Plot of Productivity

8/3/2019 SympAB

21/33

Continuous/ Gamma

Distribution

8/3/2019 SympAB

22/33

Proposed Model: M31 2 3 4

1 2 3 4( ( ))y x

b D b L b A b O C

w D w w L w w A w O C

!

8/3/2019 SympAB

23/33

8/3/2019 SympAB

24/33

Results

8/3/2019 SympAB

25/33

8/3/2019 SympAB

26/33

MRE resultsSum MRE Bias

-100 -80 -60 -40 -20 0

Estimate Checker

M3-Median

M3-Mean

M2-Median

M2-Mean

Model

Bias

Sum MRE Bias

8/3/2019 SympAB

27/33

M3-MedianM3-MeanEstimate CheckerM2-MeanM2-Median

12.5

10.0

7.5

5.0

2.5

0.0

62

45

40

1

8

40

45

30

19

5

1

28

69

45

30

19

5

1

69

30

19

45

5

69

30

5

19

1

69

45

30

5

19

1

69

8/3/2019 SympAB

28/33

Proposed Model: M4

8/3/2019 SympAB

29/33

Proposed Model: M5

8/3/2019 SympAB

30/33

M6: One Iteration

8/3/2019 SympAB

31/33

MMMI: Project level

8/3/2019 SympAB

32/33

Publications Abou Bakar Nauman, Open Source Based Generic Solution for E-Health and m-health in Pakistan: Concept

Models and Research Roadmap, Accepted at ICOSST 2011(23-25 Dec), UET Lahore, Pakistan

Abou Bakar Nauman, Software Effort Estimation Framework in Iterative and Incremental Development;Issues and Proposed Solution accepted at 3rd SAICON: International Conference on Management,Business Ethics and Economics (ICMBEE), December 28-29, 2011. Pearl-Continental Hotel Lahore, Pakistan

8/3/2019 SympAB

33/33

Summary Benefits of Bayesian Networks

Increased application is softwareengineering

Need of multi-iteration estimationmodel

Proposed model