the significance of ifpug base functionality types in effort estimation - an empirical study...

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An Empirical Study (Revised) The Significance of IFPUG Base Functionality Types in Effort

Estimation

25°International Workshop on Software

Measurement (IWSM) and 10th International

Conference on Software Process and Product

Measurement (MENSURA)

Krakow (Poland) - October 5-7, 2015

PIFs for Projects

(PifPro’15)

Luigi Buglione

Cigdem Gencel

http://www.deiser.com/


http://www.iwsm-mensura.org/2015/pifpro

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www.eng.it 4 PifPro’15– Krakow, Oct 5 2015 – © 2015 L.Buglione, C.Gencel

BFC Types Goals of the presentation

G1. Help project managers and estimators to obtain better estimates using the same historical data

G2. Propose a list of filtering criteria helping in obtaining better homogeneous clusters for data analysis and process improvements

G3. Identify and manage 'not visible' outliers in your own historical data

G4. Go into a deeper detail when gathering more granular data in your historical database, that help in consolidating CMMI ML2 goals and achieving faster ML3 ones with better PALs (Process Asset Libraries)

G5. Stimulate improvements in your organization supporting more and more experience by quantitative data depicting projects’ profiles



BFC Types Agenda

• Introduction

– A FSM History

– Estimation Techniques

– Top 10 Measurement problems

– Estimation and SPI

• Related works

• Empirical Study

– Data Collection

– Data Preparation

– Statistical Analysis & Results

• Conclusions & Prospects

• Q & A



Introduction Why profiling?



Introduction A FSM History

Source: FSM webpage: http://www.semq.eu/leng/sizestfsm.htm


http://www.semq.eu/leng/sizestfsm.htm










Introduction Estimation Techniques

Source: Briand L., Wieczorek I., Resource Estimation in Software Engineering, ISERN Technical Report 00-05, International Software Engineering Research Network, 2000, URL: http://isern.iese.de/moodle/


http://isern.iese.de/moodle/








Introduction Top-10 Problems in Measurement

1. Betting the Measurement Program on a Single Metric; 2. Trying to Find a Single Metric that Solves All Problems and Has No Evils 3. The Quest for an Industry Standard Set of Measures 4. Not Linking Measures to Behaviour; Failing to Realize that the

Measures Are the System 5. Assuming that One Set of Measures Will Be Good for "All Time" 6. Measuring the Wrong IT Output 7. Measuring in Business Terms, but the Wrong Business Terms 8. Failure to Quantify in Business Terms; Failure to Plan for Benefits 9. Neglecting the Full Range of IT-Related Outcomes 10. Lack of Commitment; Treating Measurement As a Non-Value-Added

Add-On

Source: Rubin H.A., The Top 10 Mistakes in IT Measurement, IT Metrics Strategies, Vol.II, No.11, November 1996, URL: http://goo.gl/YhRBos


http://goo.gl/YhRBos

http://goo.gl/YhRBos


Introduction Estimation and SPI (CMMI-DEV, ML2)

MA – Measurement & Analysis PP – Project Planning

PMC – Project Monitoring & Control REQM – Requirement Mgmt

SG1 Establish

Estimates

SG2 Develop a

Project Plan

SG3 Obtain

Committment

to the Plan

Measurement Data

An agreed-to set of requirements

Planning Data

Project Plans



Introduction Estimation and SPI (CMMI-DEV, ML3)

Senior Management

Project Mgmt, Support &

Engineering PAs

OT Org.

Training

OPF Org.

Process Focus

OPD Org.

Process

Definition Improvement Information (e.g. lessons learned, data, artifacts)

Process Improvement proposals; participation in definining, assessing, and

deploying processes

Resources and Coordination

Std processes and other assets

Training for projects and support groups in std process and assets

Organization’s business objectives

Std process, work

environment std, and other assets



Introduction Estimation and SPI (CMMI-DEV, ML3 - OPD)

Create Org.

Process Assets

SP1.2 Establish lifecycle model

descriptions

SP1.3 Establish Tailoring Criteria &

GL

Make Supporting Process Assets

Available

SP1.4 Establish

Org. Meas. Repository

SP1.5 Establish Org. PAL

SP1.6 Establish

Work Env. Std

Lifecycle models

Org. Standard Processes

Org. Measur. Repository

Org. Library of Process Doc

Tailoring Guidelines

SP1.1 Establish Standard Processes



Source: Gencel C. & Buglione L., Do Different Functionality Types Affect the Relationship between Software Functional Size and Effort?, Proceedings of IWSM/MENSURA 2007, Palma de Mallorca (Spain), November 5-8 2007, pp. 235-246

)()()()()(_ 543210 EIFBILFBEQBEOBEIBBEffortNW

Use more independent variables

• when using FSM methods, e.g. use combinations of 2+ BFC types

IFPUG BFC (EI, EO, EQ, ILF, EIF)

COSMIC BFC (E, X, R, W)

• Results: increased R2 using the same dataset

Preconditions

• Historicize project data at the proper level of granularity. E.g. FSU at the BFC type level (by frequencies and – eventually – weigthed values)

Effort at the SLC phase and/or by ReqType and/or…

Defects by severity/priority class and/or resolution time by phase, and/or…

• Skill people – not only estimators – a bit more on Statistics

• Use something more than averages!

Related Works Analysis on the use of single BFC types



Related Works

Study/Year Obs Source FSMM Filters R2 w/CFP

R2 w/BFC

Diff. %

Buglione-Gencel (2008)

34 ISBSG r10 COSMIC

v2+ DQR/NewDev 0.7639 0.8919 +16.7

30 ISBSG r10 COSMIC v2+

DQR/Enh 0.7086 0.8755 +23.6

Bajwa-Gencel (2009)


DQR/ApplType (2)

0.29 0.78 +64.1


DQR/ApplType (3)

0.29 0.86 +66.3

Ferrucci-Gravino-Buglione (2010)

15 Company’s data

COSMIC v2.2

Web-based portals (all)

0.824 0.875 +5.82

8 Company’s data

COSMIC v2.2

Web-based portals (subset

1)

0.910 0.966 +5.79

7 Company’s data

COSMIC v2.2

Web-based Inf. Utilities (subset

2)

0.792 0.831 +4.69

Analysis on the use of single BFC types



Empirical Study Data Collection (ISBSG r11, 2009)

FSMM No. Projects % of the projects

IFPUG 3.799 75%

FISMA 496 10%

COSMIC 345 7%

Others (LOC, Dreger, etc.) 221 4%

NESMA 155 3%

Mark-II 36 1%

Total 5.052 100%



Empirical Study Data Collection (ISBSG r11, 2009)

Entity Attribute Definition

Product Count Approach The description of the technique that was used to size the project (e.g. IFPUG, COSMIC, etc.)

Product Functional Size The count of unadjusted FP. The unit is based on the measurement method that is used to measure the functional size.

Product Application Type The type of the application (e.g. MIS).

Project Normalized Work Effort

The effort used during the full life cycle. For those projects that have covered less than a complete life cycle effort, this value is an estimate. For those projects covering the full life cycle and those projects whose development life cycle coverage is not known, this value and value of summary work effort is same.

Project Development Type This field tells that whether the development is new, enhanced or re-developed

Project Business Area Type

This identifies the subset within the organisation being addressed by the project. It may be different to the organisation type or the same. (e.g.: Manufacturing, Personnel, Finance).

Project Programming Language Type

The primary language used for the development: JAVA, C++, PL/1, Natural, Cobol etc.



Empirical Study Data Preparation

Step Attribute Filter Projects Excluded

Remaining Projects

0 --- --- --- 5052

1 Count Approach = IFPUG 1,253 3,799

2 Data Quality Rating (DQR) = {A | B} 3,799 3,614

3 Quality Rating for Unadjusted Function Points (UFP)

= {A | B} 3,614 2,879

4 BFC Types = {Not Empty} 1,482 1,397

Four subsets derived:

ID # projects

Dev Type

Application Type Bus. Type Prog.Lang.

1 37 NewDev Fin trans. Process/accounting Insurance All

2 14 NewDev Fin trans. Process/accounting Insurance COBOL

3 15 NewDev Fin trans. Process/accounting Insurance Visual Basic

4 16 NewDev Fin trans. Process/accounting Banking COBOL



Empirical Study Statistical Analysis & Results - UFP

A typical elaboration (subset #3) only with UFP…

Linear Regression Statistics

R 0.817

R Square 0.667

Stand. Error 2911.091

Total Number Of Cases 15

ANOVA

d.f. SS MS F p-level

Regression 1. 220,988,529.59 220988529.59 26.08 0.00

Residual 13. 110,167,824.81 8474448.06

Total 14. 331,156,354.40

Coeff. Std Err LCL UCL t Stat p-level

H0 (2%) rejected?

Intercept 2149.62 849.57 -102.01 4401.26 2.53 0.03 No

Total (IFPUG FP) 3.97 0.78 1.91 6.03 5.11 0.00 Yes

T (2%) 2.65



Empirical Study Statistical Analysis & Results – BFC+

..and applying more BFCs

Linear Regression Statistics

R 0.932

R Square 0.868

Stand. Error 2205.569

Total Number Of Cases 15

ANOVA

d.f. SS MS F p-level

Regression 5. 287375530.43 57475106.09 11.82 0.00

Residual 9. 43780823.97 4864536.00

Total 14. 331156354.40



Empirical Study Statistical Analysis & Results – BFC+

..and applying more BFCs (…next)

Coeff. Std Error LCL UCL t Stat p-level

H0 (2%) rejected?

Intercept 2076.14 878.79 -403.31 4555.59 2.36 0.04 No

EI -14.74 39.13 -125.16 95.67 -0.38 0.72 No

EO 4.67 36.98 -99.67 109.01 0.13 0.90 No

EQ 26.25 9.81 -1.44 53.93 2.67 0.03 Yes

ILF -24.26 12.58 -59.76 11.23 -1.93 0.09 No

EIF 34.85 14.23 -5.29 74.99 2.45 0.04 Yes

T (2%) 2.90



Empirical Study Statistical Analysis & Results

Subset # prj R2

w/Total FP

Is Total FP significant?

R2 w/FP for each BFC Type

Diff% (R2)

Which BFC Types are

significant?

#1 37 0.290 Yes 0.369 +21% No

#2 14 0.057 No 0.838 +93% Yes (ILF)

#3 15 0.667 Yes 0.868 +23% Yes (EQ, EIF)

#4 16 0.720 Yes 0.893 +19% Yes (EO)

Data set # points EI EO EQ ILF EIF

Subset1 37 16.9% 24.6% 19.3% 21.7% 17.6%

Subset2 14 19.8% 39.0% 6.3% 14.4% 20.6%

Subset3 15 17.0% 21.6% 22.8% 23.4% 15.3%

Subset4 16 18.7% 31.0% 11.4% 27.7% 11.2%

% distribution of BFC types by value

Summary Data



BFC Types Conclusions & Perspectives • FSM Methods

Born with the goal to provide more objectivity in sizing FUR for a software system The IFPUG method has the heritage of the Albrecht’s FPA and evolves it from 1986 Current version is v4.3.1 (Jan 2010) and is also an ISO standard (20926:2009) Several methods have arisen and share common principles and background (ISO 14143-x)

• BFC Types Each FSM method has a series of basic countable elements contributing to the final fsu

value, generically called by ISO “BFC” IFPUG FPA has 5 BFC: EI, EO, EQ, ILF, EIF Regression analysis with ANOVA

Sizing & Estimation issues R2 values increased in 3 out of 4 cases (from +19% till +93%) Programming language (no set in subset #1) can impact in absolute terms on

predictability Some lessons learned

Positive Effects: using that approach yet at lower maturity levels (e.g. ML2) can improve significantly estimates, helping in saving resources to be reinvested in other project activities, anticipating also the achievement of ML3 concepts (e.g. PAL) functional profiles

Precondition: gather historical FSM data at that level of granularity …let’s remember when estimating anyway that any fsu is a product size for software FURs

(and not a project size) deal with NFR and their impact on the overall project effort within the defined project scope

New issues ISBSG D&E r13 increased the number of projects to 6670, more fields (also for Agile

projects) Same analysis (and profiles) can be investigated for nfsu (e.g. using non-functional

models/techniques) for depicting non-functional profiles



Lessons Learned... BFC Types

UR

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http://www.dilbert.com/






Q & A

Dziękuję za uwagę!

Thanks for your attention!

BFC Types



Our Contact Data

Cigdem Gencel Deiser

[email protected]

Luigi Buglione

Engineering Ingegneria Informatica/ETS [email protected]

BFC Types


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