metrics for effort/cost estimation of mobile apps development

Università degli studi di SalernoDipartimento di Scienze Aziendali, Management & Innovation SystemCorso di Laurea Magistrale in Tecnologie Informatiche e Management

Metrics for Effort/Cost Estimation of Mobile apps development

ANNO ACCADEMICO 2015-2016

Relatore: Prof. ssa Filomena FerrucciDott. Pasquale Salza

Candidata: Catolino Gemma

Matricola 0222500095

Tesi di laurea magistrale in Ingegneria del Software: Metriche, Qualità e Valutazione Sperimentale

The Effort and Cost Estimation

Not Only for traditional Software

always hard to estimate in advance

over budget

and overrun

Continuous process

When more data become available…

more accurate estimations can be achieved!

Non-Model-Based

Human experts

MAN / HOURS

Model-BasedM M M M

MAN / HOURS

the Size Factor

the Size Factor

L OC

L OC

the Size Factor

F P AFunction Point Analysis

(functional) transactions

and (logical) data

the Size Factor

c f pCosmic Function Point

movements from/to persistent

storage and users

the Size Factor

D’avanzo et al. approach

van Heeringen & van Gorp

approach

Sellami et al.

Set of guidelines for

an approximate and

quick sizing of mobile apps

IFPUG Guidelines

D’avanzo approach

van Heeringen & van Gorp

approach

Cozzolino et al. approach

new set of guidelines

Cozzolino et al. approach

View/Show Data

Create/Set/Delete Data

Invoking service

new set of guidelines

Cozzolino et al. approach

3 CFP

3 CFP

2 CFP

new set of guidelines

LIMITations

The software life cycle is already started!

Early Effort Estimation

Defining a set of metrics for mobile early effort estimation

Defining a set of metrics for mobile early effort estimation

Investigating how the early size measure can be mapped

into Cozzolino et al. guidelines

Defining a set of metrics for mobile early effort estimation

Investigating if the mapping is useful for estimating CFP

Investigating how the early size measure can be mapped

into Cozzolino et al. guidelines

Defining a set of metrics for mobile early effort estimation

Emilia Mendes

Emilia Mendes, Nile Mosley, and Steve Counsell.Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.

Analysis of quote form

Emilia Mendes, Nile Mosley, and Steve Counsell.Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.

Emilia Mendes, Nile Mosley, and Steve Counsell.Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.

377manually validated links

Analysis of quote form

Analysis of quote form

Emilia Mendes, Nile Mosley, and Steve Counsell.Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.

Extraction of initial set of metrics

Features

Categories

Features Application GUI

Categories

Features Application GUICost

Driver

Categories

Features Application GUI

Project’s Metrics

Cost Driver

Categories

Features Application GUI

Project’s Metrics

Cost Driver

Application functionality

Categories

Features Application GUI

Project’s Metrics

Cost Driver

Application functionality

Application size

Categories

Features Application GUI

Project’s Metrics

Cost Driver

Application functionality

Possible Metrics

Application size

Categories

Emilia Mendes, Nile Mosley, and Steve Counsell.Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.

Extraction of initial set of metrics Validation of initial

set of metrics

Analysis of quote form

Validation of initial set of metrics

42

DEVELOPERS

PROJECT MANAGERS

Validation of initial set of metrics

TWO SURVEYS

Validation of initial set of metrics

TWO SURVEYS

Validation of initial set of metrics

TWO SURVEY

YY

Validation of initial set of metrics

48 METRICS

Validation of initial set of metrics

36CONFIRMED

Validation of initial set of metrics

12DELETED

Validation of initial set of metrics

12DELETED

Project start date

App purchasing

Type of business owns the app idea

Complex back-end

Validation of initial set of metrics

5 ADDED

Validation of initial set of metrics

5 ADDED

Support Security

Backward compatibility

User target

Features

Generalities

Projects Design

Platfom

Accounting

User featuresSocial Aspect

Remote Connection

eCommerce

Date & Location

MonitoringAdditional

Functionality

Renovation of Categories

DesignProjects

Platfom

Features

Date & Location

MonitoringAdditional

Functionality

Renovation of Categories

Accounting

User featuresSocial Aspect

Generalities

Remote Connection

eCommerceGoogle Module

SIZE

Investigating how the early size measure can be mapped into Cozzolino et al. guidelines

Requirements

Early phase of developments Requirement Elicitation/ Analysis

SOFTWARE SIZE

Early phase of developments Requirement Elicitation/ Analysis

Early phase of developments Requirement Elicitation/ Analysis

cosmic

View/Show Data

Exchange Data via a network

Invoking service

Create/Set/Delete Data

GuidelinesCozzolino et al.

Early MetricsSocial sharing

Search

MessagingAd hoc

authentication

Analytics

Exchange Data via a network

Early Metrics

Ad hoc authentication

GuidelinesCozzolino et al.

Exchange Data via a network

Lines guideCozzolino et al.Early Metrics

Ad hoc authentication

MIN MAX10 CFP5 CFP

Login + RegisterLogin

Ad hoc authentication

Exchange Data via a network

Lines guideCozzolino et al.Early Metrics

Ad hoc authentication

MINMAX10 CFP 5 CFP

Login + Register Login

41 METRICS

Exchange Data via a network

Lines guideCozzolino et al.Early Metrics

Ad hoc authentication

MINMAX10 CFP 5 CFP

Login + Register Login

26 METRICSMIN MAXOPERATIONS

Empirical study

Evaluate the accuracy of the estimations in terms of

COSMIC Function Pointsof the early metrics

RQ: To what extent the CFPs extractable using the early metrics are close to the actual CFPs of a Mobile app?

Evaluate the accuracy of the estimations in terms of

COSMIC Function Pointsof the early metrics

Evaluate the accuracy of the estimations in terms of

COSMIC Function Pointsof the early metrics

13 MOBILE APPLICATIONS

APP FUR EARLYMETRIC

DESIGN

EARLYMETRIC

#CFP

DESIGN

MIN

MAXAVG

EARLYMETRIC

#CFP

MRE

MMRE

MdMRE

PRED(25)

DESIGN

Results

Application Early CFP_min Early CFP_max Early CFP_avg Oracle

Wikipedia 37 47 42 46

Munch 41 51 46 42Loopboard 16 21 18,5 14

Man man 34 44 38,5 38

Easy Sound Recorder 20 25 22,5 18

K-9 Mail 38 53 45,5 32

Transportr 47 67 57 38

Hashr 23 23 23 19

arXiv Mobile 37 42 39,5 39NPR News 37 42 39,5 38

Loop Habit Tracker 26 31 28,5 28

Radio Droid 33 38 35,5 50

RoomMates Expense 26 31 28,5 44

Application Early CFP_min Early CFP_max Early CFP_avg Oracle

Wikipedia 37 47 42 46

Munch 41 51 46 42Loopboard 16 21 18,5 14

Man man 34 44 38,5 38

Easy Sound Recorder 20 25 22,5 18

K-9 Mail 38 53 45,5 32

Transportr 47 67 57 38

Hashr 23 23 23 19

arXiv Mobile 37 42 39,5 39NPR News 37 42 39,5 38

Loop Habit Tracker 26 31 28,5 28

Radio Droid 33 38 35,5 50

RoomMates Expense 26 31 28,5 44

Application Early CFP_min MRE_min PRED(25)

Wikipedia 37 0,19 1

Munch 41 0,02 1Loopboard 16 0,14 1

Man man 34 0,01 1

Easy Sound Recorder 20 0,11 1

K-9 Mail 38 0,19 1

Transportr 47 0,24 1

Hashr 23 0,21 1

arXiv Mobile 37 0,05 1NPR News 37 0,03 1

Loop Habit Tracker 26 0,07 1

Radio Droid 33 0,34 0

RoomMates Expense 26 0,41 0

MIN

MIN

MMRE 0,16

MDMRE 0,14

PRED(25) 85%

Application Early max MRE_max PRED(25)

Wikipedia 47 0,02 1

Munch 51 0,21 1Loopboard 21 0,5 0

Man man 44 0,16 1

Easy Sound Recorder 25 0,39 0

K-9 Mail 53 0,66 0

Transportr 67 0,76 0

Hashr 23 0,21 1

arXiv Mobile 42 0,08 1NPR News 42 0,1 1

Loop Habit Tracker 31 0,11 1

Radio Droid 38 0,24 1

RoomMates Expense 31 0,29 1

MAX

MIN

MMRE 0,29 0

MDMRE 0,21

PRED(25) 61%

Application Early avg MRE_avg PRED(25)

Wikipedia 42 0,09 1

Munch 46 0,09 1Loopboard 18,5 0,32 0

Man man 38,5 0,01 1

Easy Sound Recorder 22,5 0,25 1

K-9 Mail 45,5 0,42 0

Transportr 57 0,5 0

Hashr 23 0,21 1

arXiv Mobile 39,5 0,01 1NPR News 39,5 0,04 1

Loop Habit Tracker 28,5 0,02 1

Radio Droid 35,5 0,29 0

RoomMates Expense 28,5 0,35 0

AVG

MMRE 0.2

MDMRE 0.21

PRED(25) 61%

AVG

Application Early CFP_min Early CFP_max Early CFP_avg Oracle

Wikipedia 37 47 42 46

Munch 41 51 46 42Loopboard 16 21 18,5 14

Man man 34 44 38,5 38

Easy Sound Recorder 20 25 22,5 18

K-9 Mail 38 53 45,5 32

Transportr 47 67 57 38

Hashr 23 23 23 19

arXiv Mobile 37 42 39,5 39NPR News 37 42 39,5 38

Loop Habit Tracker 26 31 28,5 28

Radio Droid 33 38 35,5 50

RoomMates Expense 26 31 28,5 44

Application Early CFP_min Early CFP_max Early CFP_avg Oracle

Wikipedia 37 47 42 46

Munch 41 51 46 42Loopboard 16 21 18,5 14

Man man 34 44 38,5 38

Easy Sound Recorder 20 25 22,5 18

K-9 Mail 38 53 45,5 32

Transportr 47 67 57 38

Hashr 23 23 23 19

arXiv Mobile 37 42 39,5 39NPR News 37 42 39,5 38

Loop Habit Tracker 26 31 28,5 28

Radio Droid 33 38 35,5 50

RoomMates Expense 26 31 28,5 44

RQ: To what extent the CFPs extractable using the early metrics are close to the actual CFPs of a Mobile app?

The estimations provided by our metrics resulted quite

close to the actual values

EARLYMETRIC

Additional validation with companies

Gather data

FUTURE WORK

FUTURE WORK

EARLYMETRIC CFP+

Summary

Summary

Summary

Summary

Summary

Summary

Thank you!