Building Productivity Models for Small Enhancements

doi:10.4236/jsea.2013.63016

Paper Menu >>

Journal Menu >>

Journal of Software Engineering and Applications, 2013, 6, 121-130

http://dx.doi.org/10.4236/jsea.2013.63016 Published Online March 2013 (http://www.scirp.org/journal/jsea)

121

Building Productivity Models for Small Enhancements

Jean-Marc Desharnais1,2, Gülşah Yıldızoğlu1, Alain April2, Alain Abran2

1Boğaziçi University, Istanbul, Turkey; 2Department of Software Engineering and IT, École de Technologie Supérieure, Montréal,

Canada.

Email: [email protected]

Received January 9th, 2013; revised February 10th, 2013; accepted February 18th, 2013

ABSTRACT

Software is in constant evolution and many approaches have been suggested to study software maintenance productivity.

This research reports on a process to design and implement a productivity model of legacy software based on the meas-

urement of small functional enhancements using the COSMIC ISO 19761 international standard. Two motivations in-

fluence this research: 1) understanding the productivity of the software maintenance process to help manage the cost of

maintenance; 2) understanding the cost drivers that affect the software maintenance productivity. This research reports

on an empirical study of a productivity measurement program implemented in a large banking legacy system.

Keywords: Small Enhancements; Productivity; Productivity Models; Maintenance; Cost of Maintenance

1. Introduction

1.1. Definition of Maintenance

The software lifecycle can be divided into two distinct

parts, as presented in ISO 12207 [1]: the initial develop-

ment of the software and its use and ongoing mainte-

nance.

The international standard ISO 14764 [2] on software

maintenance defines four categories to classify the nature

of individual maintenance work requests: adaptive, cor-

rective, preventive, and perfective (see Table 1).

ISO 14764 classifies adaptive and perfective mainte-

nance as enhancements, and the corrective and preven-

tive maintenance as corrections [2]. This research is fo-

cused mainly on adaptive and corrective work requests

where most of the changes to functionality occur.

Table 1. ISO 14764 Software maintenance categories.

Category Description

Adaptive

Modifications to adapt a software product to

change in data requirements and

processing environments

Corrective

Reactive modification of a software product

performed after delivery to correct the faults

discovered. These modifications often repair code

to satisfy functional requirements

Preventive

Modification of a software product after delivery

to detect and correct latent faults before they

become operational faults

Perfective

Modification of a software product after delivery

implementing new or changed user requirements

which concern functional enhancements

to the software

In large organizations, most of the IT personnel are as-

signed to software maintenance—see Table 2 [3]. How-

ever, software maintenance is still a rather neglected ac-

tivity by both IT managers and academic research ac-

cording to a number of authors (Torchiano, Ricca, and

De Lucia [4], Koskinen [5], Kuhlmann [6]). Already in

1996 Basili et al. were reporting that there were not

enough empirical studies and available research data for

software maintenance [4] while Koskinen [5] was report-

ing that “Software maintenance and evolution is a con-

siderably understudied area while taking into account its

cost effect”. Many of the empirical studies on software

maintenance management date back to the pre-2000,

such as Abran [7], Lehman [8], Genuchten [9], Arfa [10],

Desharnais [11] and others.

Table 2 published in 2006 shows that between 2000

and 2005 there was an increase of nearly 4% of the

maintenance personnel in USA [12]. The projection for

the following years is showing that the number of per-

sonnel in maintenance would increase considerably over

Table 2. USA software personnel in software development

and maintenance—2006 [12].

Year Development

personnel

Maintenance

personnel

Total

personnel

Maintenance

percent

2000 750,000 2,000,000 2,750,000 72.73%

2005 775,000 2,500,000 3,275,000 76.34%

2010 800,000 3,000,000 3,800,000 78.95%

2015 1,000,0003,500,000 4,500,000 77.78%

2020 1,100,0003,750,000 4,850,000 77.32%

2025 1,250,0004,250,000 5,500,000 77.27%

Building Productivity Models for Small Enhancements

122

the next 20 years.

1.2. Research Motivation

This paper presents an empirical study of 88 small func-

tional enhancements to a software system from the core

banking ERP legacy system of a large retail bank to fig-

ure out the productivity of a maintenance process. More

specifically, the motivations of this paper are: 1) to un-

derstand the productivity of the software maintenance

process to help manage the cost of maintenance; and 2)

to understand the cost drivers that affect the software

maintenance productivity.

Benestad et al. [13] presents an overview of recent ap-

proaches by researchers for software maintenance im-

provements: maturity models to help with software main-

tenance process improvement initiatives [14,15], estima-

tion of maintenance costs [16], insights into the precon-

ditions for measuring software maintenance productivity

[14,17] and direct analysis of productivity models [4,10].

As suggested by those authors, the main hypothesis in

the design of productivity models specific to software

maintenance is that such models should distinguish be-

tween the product, resources and processes data. More

specifically, Benestad et al. reports that “Investigations

into cost drivers during software maintenance and evolu-

tion have investigated the effects of project properties

such as maintainer skills, team size, development prac-

tices, execution environment, and documentation” [13].

In this organization, IT management was interested in

understanding and updating their existing productivity

models for the following reasons:

- Productivity data on software maintenance was no

longer up to date (older than 10 years);

- Leveraging the new 2nd generation of functional size

measurement method, namely the COSMIC ISO

19761 measurement method [18] independently of the

programming language technology and with a finer

measurement granularity [19];

- A productivity model built from an up to date data

sample more representative of their current software

portfolio;

- A capability to tackle both management and custom-

ers questions about software maintenance productivity.

The rest of this paper is structured as follows. Section

2 presents the data collection and the process to build a

productivity model for software maintenance. Section 3

presents the data set of the case study. Section 4 presents

the data analysis and, Section 5 presents the conclusion

and future work.

2. The Data Collection Process

For this empirical study, the productivity model is built

in two phases:

1) A rigorous data collection process to collect reliable

data—steps 1 to 5 in Figure 1.

2) Implementing a rigorous process to design a pro-

ductivity model—steps 6 and 7 in Figure 1.

The following sub-sections explain the building pro-

cess of the productivity model as sketched in Figure 1.

2.1. Identification of Each Small Enhancement

A change request (CR) is defined in ISO 14764 [2] as

“proposed changes to a product that is being maintained”.

The data was collected through the system change re-

quest (CR) process implemented at the bank. The main-

tenance manager then looks at individual request, priori-

tizes it and, next, assigns it to a maintainer who will con-

duct the impact analysis and address it. A specific team

handlesthe maintenance requests received for one spe-

cific system module.

Figure 1. Steps to design the productivity model.

Building Productivity Models for Small Enhancements 123

Adaptive and corrective maintenance:

 The enhancements with the effort less than 3 weeks

of effort1 were selected for analysis.

 All from the same core banking application, ensuring

the same application domain (i.e., management infor-

mation system—MIS), programmed on an IBM main-

frame environment: e.g., COBOL, PL/1 or tools like

Cool:Genusing DB2 or Oracle databases.

2.2. Measurement of the Size of Each Small

Enhancement

Each 88 small enhancements, being a functional process,

represent enough data points to fulfill good statistical

conditions for the sixth criteria. These small enhance-

ments (adaptive and corrective only) were designed, pro-

grammed and implemented on the same large core bank-

ing ERP application from April 2010 to December 2011.

Each small enhancement had documented and well un-

derstood characteristics, in the change request system of

the bank, such as: the programming language used, types

(i.e. batch vs. online), and tools used—see Appendix A

for more details.

These 88 small functional enhancements were meas-

ured by the same “maintainer” and next verified by an

independent COSMIC certified expert. In this step, the

COSMIC [18] measurement method was used to deter-

mine the size of each small enhancement. To measure the

size of a small enhancement, all of its functional proc-

esses were identified, and then measured in terms of

COSMIC function points (CFP). The maintainer who

applied the measurement method was the one who ana-

lyzed, programmed and implemented each small enhan-

cement. Afterwards, an independent functional measure-

ment expert verified the number of CFP obtained for

each functional process based on the documentation of

each small enhancement (see examples in Appendix A).

Each small enhancement had a functional size of less

than twenty CFP (COSMIC Function Points).

2.3. Determination of the Quality of the

Information Provided for Each Small

Enhancement

The documentation was produced and controlled by the

same maintainer who had implemented the small en-

hancements. The quality of the documentation of each

small enhancement was assessed based on the COSMIC

verification process requirements [19]. This activity

helps verify the quality of the functional size results. The

determination of the quality of the documentation can be

assessed based on: 1) the presence or absence of a data

model; 2) the availability of the description of the data

movements; and 3) the identification of each functional

process in the software. Because the maintainer who had

done the maintenance was present during this exercise, it

was possible to complete the documentation, when need-

ed. The resulting quality of the documentation related to

small enhancements used in this case study is considered

as high (i.e., of very good quality) based on the quality

rating (Appendix A).

2.4. Collection of Effort for Each Small

Enhancement

The maintainers recorded the effort information on a

daily basis.

 Duration between half a day and 3 weeks. Effort and

duration measures were available and reliable.

 A small team of 1 or 2 maintainers executed every

small enhancement.

2.5. Collection of Characteristics of Each Small

Enhancement

The data available to analyze characteristics are: 1) func-

tional size [18]; 2) maintenance categories (adaptive and

corrective maintenance types are handled); 3) develop-

ment tool (Cool:Gen versus PL/1); 4) batch versus online

programs. Cost drivers are used in this empirical study to

analyze their influence on the effort.

2.6. Determination of Unit Cost for Each Small

Enhancement

After the maintenance requests are ordered according to

efforts, the number of functional points per hour is used

to calculate the productivity ratio while the unit cost is

determined by dividing effort (input) required to develop

small enhancements to functional size (output) of each

small enhancement.

Effort

Functional Size

2.7. Constructing Productivity Model(s) for

Small Enhancements

A productivity model is typically built with data from

CRs completed, when all information on a CR is avail-

able and that there is no more uncertainty: all of the

software functions have been delivered and all of the

number of hours for the project have been completed and

measured.

3. Presentation of the Dataset

This section presents a descriptive analysis of the dataset.

Table 3 shows the minimum, maximum, average and

standard deviation of the functional size (in number of

CFP) and effort (in person-hours) of this data set. In

1It was decided by the organization analyzed that if an enhancement

works take more than three weeks it fall in another category.

Building Productivity Models for Small Enhancements

124

Table 3. Distribution of CFP and hours.

Min. Max. Avg. Std. Dev.

CFP 3 13 5.0 2.1

Effort 1 40 9.9 8.4

Table 3, the functional size of the for small enhance-

ments varies from 3 to 13 CFP, while the effort in hours

varies from 1 to 40 hours. The average functional size of

a small enhancement is 5 CFP with a standard deviation

of 2.1 CFP. The average number of hours is 9.9 with a

standard deviation of 8.4 hours.

Table 4 presents the others variables. Those variables

are related to the programming (PL/1 or Cool:Gen), the

access type (Batch or Online) and the type of main-

tenance (adaptive or corrective). Since these variables are

descriptive, they must be transformed into dummy vari-

ables, where eachcandidate value isbinary (either 0 or 1:

presence or absence) for each of those category of vari-

able .

In Tabl e 4, the number of small enhancements pro-

gram in PL/1 is 39, and in Cool:Gen 49. The types of

access for small enhancements are respectively 39 and 49,

and the type of enhancement are 51 for adaptive and 26

for corrective. There is no information for 11 small

enhancements or they are mixed. Also, when looking at

the data (Appendix A) it shows that the PL/1 is always

associated with Batch and Cool:Gen with Online. For

this reason it is not possible to analyze those variables

independently.

4. Analysis of the Data

SPSS was used to analyze the data with regression statis-

tics. The acceptance of regression results hinges on di-

agnostic checking for the acceptance of “classical as-

sumptions” [20]. In software engineering, the classical

assumption is that the size should explain 70% of the

effort 80% of the time. For this analysis, if the regression

analysis is showing a result greater than 0.70, this will

confirm the “classical assumption” [20].

4.1. Regression Analysis Procedure

To proceed for a regression analysis it is necessary to

follow the procedure commended in [20].

A) Steps before starting the regression:

1) Define the methodology to build the regression

model for productivity (Section 2).

2) Decide which variable will be used in the model.

3) Choose the statistical tool and enter the data for sta-

tistical analysis.

4) Use descriptive analysis to find outliers. From the

descriptive statistics no outlier is present for the quantita-

tive variables (functional size and effort) in the data set

Table 4. Others variables.

Variable Category Number of enhancements

PL/1 39

Programming

language Cool:Gen 49

Batch 49

Type of programs Online 39

Adaptive 51

Type of maintenanceCorrective 26

used.

B) Steps for the regression analysis:

1) Choose which variables will be analyzed.

2) Run a regression analysis.

3) Present the results.

4) Interpret the results.

5) Accept or reject the results (in the productivity mo-

del).

6) Start again with new choices of variables.

4.2. Linear Regression Models

It is not possible to present all the results. Only some of

them will be presented following the proposed steps:

1) Choose which variables will be analyzed.

In this regression analysis, the functional size of small

enhancements is selected as the dependent variable and

effort as the independent variable in the model. This

analysis aims to find the relation between functional size

and effort: Effort = function (functional size in CFP)

2) Run a regression analysis.

SPSS was chosen to run a linear regression analysis

with the ANOVA (Table 4).

Table 5 shows the regression analysis and the

ANOVA, and Figure 2 the corresponding regression plot.

In Figure 2 the regression plot is based on 88 small en-

hancements points at the intersection of the size (CFP)

and the effort. There is no obvious outlier that can be

seen.

3) Interpret the results.

The coefficient of determination (i.e. R2) result is 0.75.

This study considers that a R2 of 0.70 is acceptable in

software engineering. For the Anova in Table 5, the last

column Sig. shows the goodness of fit of the model. If

this number is smaller than 0.01 then the model is sig-

nificant at 99%, if it is smaller than 0.05 then the model

is significant at 95%, and if it less than 0.1 the model is

significant at 90% [20]. Significance implies the accep-

tance of the model: the lower this number, the better it

fits. For the Anova in Ta ble 5, the Sig. value of 0.000

confirms the significance of the model at more than 99%.

The result is acceptable from the goodness of the fit.

The number of CFP explains more than 75% of the

Building Productivity Models for Small Enhancements 125

Table 5. Regression analysis and Anova.

Variables Entered/Removeda

Model Variables

Entered

Variables

Removed Method

1 CFPb Enter

aDependent variable: Effort; bAll requested variables entered.

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 0.870a 0.757 0.754 4.155

aPredictors: (Constant), CFP.

ANOVAa

Model Sum of

Squares df Mean

Square F Sig.

Regression 1

Residual

Total

4,617,496

1,484,948

6,102,443

4,617,496

17,26

267,420 0,000^b

aDependent variable: Effort, bPredictors: (Constant), CFP.

variance in the effort.

4) Start again from new choices of variables.

4.3. Linear Regression Models

In the previous models, only two variables were used.

The next question is: is it possible to improve the regres-

sion value using some other variables or multiple vari-

ables?

Table 5 presents the multi-regressions results for the

three (3) independent cost drivers analyzed, together with

the independent variable CFP, and the dependent vari-

able effort.

Table 5 shows that the R2 is constantly over 0.75,

which means that independents variables (adaptive/cor-

rective, online/batch) used for regression analysis explain

more than 75% of the variance in the effort variable. The

ANOVA still have a Sig. of 0.000 for all three multi-re-

gressions and the plot is not very useful because of the

binary nature of each variable. However, none of these

additional independent variables adds much to the ex-

planation of the relationship with functional size and

effort, which already had an R2 of 0.75, thereby, the con-

tribution of these additional variables, concurrently, is

minimal.

Average Unit Cost

This research also presents the average unit costs using

a number of cost drivers.

Table 6 shows that the average unit cost for all the

data is 1.81 hours per CFP with a difference between the

lowest and highest average of 0.9 hours for 88 small en-

hancements.

Table 7 shows that that the lowest unit cost is for

Table 6. Multi-regression models.

Independent

Variables

Size & Adaptive/

Corrective

Size &

Online/Batch

Size &

PL1/Cool:Gen

R2 0.773 0.761 0.759

Table 7. Unit cost per variable.

Variables Average hours/CFP

All data 1.8

Batch 1.6

Online 2.0

Adaptive 1.4

Corrective 2.5

Adaptive maintenance (1.4 hrs/CFP), while corrective

maintenance costs almost twice as much (2.5 hrs/CFP).

For Batch and Online the average is 1.6 and 2.0 hours/

CFP respectively.

5. Conclusions and Future Works

The following five criteria were followed in this empiri-

cal study:

1) Use of an internationally recognized functional size

measurement method to measure every small functional

enhancement, that is: the COSMIC ISO 19761 measure-

ment method [17] was used to measure the functional

size of each small enhancement (functional processes

were measured and verified).

2) Assessment of the quality of the documentation

used for the sizing of each functional enhancement.

3) A controlled environment for the maintenance per-

sonnel that worked on the enhancements.

4) Implementation of a reliable effort data collection

mechanism for each small enhancement.

5) Documentation of the individual characteristics of

each small enhancements included in this empirical study.

Enough data points (individual enhancements) are col-

lected to build a valid productivity model.

Using those criteria, it was possible to produce a pro-

ductivity model with this sample using all independent

variables (R2 of more than 0.75) of a specific large retail

bank.

The productivity is 20% better using Online, instead of

Batch mode (Table 6). Unit cost is also better (almost

60% decrease) for adaptive maintenance compare to cor-

rective maintenance.

There were a number of homogeneous empirical con-

ditions to construct this productivity model (Ta bl e 4 and

Figure 2): functional enhancements to the same major

software banking application within a single organiza-

tion, each distinct functional enhancement designed,

Building Productivity Models for Small Enhancements

126

[8] M. M. Lehman, “System Maintenance and Evolution in an

Era of Reuse, COTS, and Component-Based Systems,”

International Conference on Software Maintenance (ICSM),

Oxford, 30 August 1999.

[9] M. Van Genuchten, G. Brethouwer, T. Van den Boomen

and F. J. Heemstra, “An Empirical Study of Software

Maintenance,” Information and Software Technology,

Vol. 34, No. 8, 1992, pp. 507-512.

doi:10.1016/0950-5849(92)90144-E

[10] L. B. Arfa, A. Mili and L. Sekhri, “An Empirical Study of

Software Maintenance,” Proceedings of Conference on

Software Maintenance, Sorrento, 15-17 October 1991, pp.

52-58.

[11] J. M. Desharnais, F. Pare, M. Maya and D. St-Pierre, “Im-

plementing a Measurement Program in Software Mainte-

nance: An Experience Report Based on Basili’s App-

roach,” IFPUG Spring Conference, Cincinnati, 1997.

Figure 2. Regression plot with size & effort.

programmed and implemented by the same person, docu-

mented by the maintainer, measured within a controlled

environment and verified by a measurement expert.

[12] C. Jones, “The Economics of Software Maintenance in the

Tweenty First Century,” 2006.

[13] H. C. Benestad, B. Anda and E. Arisholm, “Understanding

Software Maintenance and Evolution by Analyzing Indi-

vidual Changes: A Literature Review,” Journal of Soft-

ware Maintenance and Evolution: Research and Practice,

Vol. 21, No. 6, 2009, pp. 349-378.

doi:10.1002/smr.412

While this type of situation is common in practice,

availability of such data for empirical analysis is scarce.

On the other hand, such homogeneity limits the gener-

alization of the results to other contexts, such as different

software applications. Availability of additional data sets

is therefore necessary for further research work. [14] A. April and A. Abran, “Software Maintenance Mana-

gement: Evaluation and Continuous Improvement,” Wi-

ley-IEEE Computer Society Press, Honoken, 2008.

doi:10.1002/9780470258033

REFERENCES

[15] M. Kajko-Mattsson, “Corrective Maintenance Maturity

Model (CM3): Maintainer’s Education and Training,”

Proceedings of the 23rd International Conference on So-

ftware Engineering, Toronto, 12 May 2001, pp. 610-619.

[1] ISO/IEC 12207, Systems and Software Engineering—

Software Life Cycle Processes, International Organization

for Standardization, Geneva, 2008.

[2] ISO/IEC 14764, Software Engineering—Software Life

Cycle Processes—Maintenance, International Organiza-

tion for Standardization, Geneva, 2006.

[16] A. Abran, “Estimation Models for Software Maintenance

Based on Functional Size,” Journal of Software Tech-

nology, Vol. 9, No. 3, 2006, pp. 18-25.

[3] M. Maya, A. Abran and P. Bourque, “Measuring the Size

of Small Functional to Enhancements Software,” The 6th

International Workshop on Software Measurement, Re-

gensburg, 19-20 September 1996.

[17] A. April, A. Abran and R. R. Dumke, “Software Mainte-

nance Productivity Measurement: How to Assess the

Readiness of Your Organization, Software Maintenance

Productivity Measurement,” IWSM/Metrikon, 2004.

[4] J. Koskinen, “Software Maintenance Costs,” University of

Jykäskylä, Finland, 2010. [18] Measurement Manual v3.0.1 (The COSMIC Implementa-

tion Guide for ISO/IEC 19761: 2003), 2009, The Com-

mon Software Measurement International Consortium

(COSMIC), 2012.

[5] M. Torchiano, F. Ricca and A. De Lucia, “Empirical

Studies in Software Maintenance and Evolution,” IEEE

International Conference on Software Maintenance, Paris,

2-5 October 2007, pp. 491-494. [19] The COSMIC Functional Size Measurement Method Ver-

sion 3.0.1 Guideline for Assuring the Accuracy of Meas-

urements Version 0.92, Common Software Measurement

International Consortium, 2011.

[6] U. Kuhlmann, “Maintenance Activities in Software Proc-

ess Models: Theory and Case Study Practice,” Master

Thesis, University of Koblenz Landau, Koblenz, 2003, pp.

1-135. [20] Regression Explained in Simpler Terms, A Vijay Gupta

Publication, SPSS for Beginners, 2000.

https://mywebspace.wisc.edu/rlbrown3/web/library/regres

sion_explained.pdf

[7] A. Abran and H. Nguyenkim, “Analysis of Maintenance

Work Categories through Measurement,” IEEE Interna-

tional Conference on Software Maintenance, Sorrento,

15-17 October 1991, pp. 104-113.

Building Productivity Models for Small Enhancements 127

Appendix A. List of the Small Enhancements with Measures

No Identification E R W XCFPEffortTypeRatioToolQualityBatch Online Write ExitModule

1 Changing General Ledger

Numbers of Retail Credit 1 1 1 1 44 M 1.00PL/IA Y N 0 0 1

2 Changing General Ledger

Numbers of Commercial Credits1 1 1 1 44 M 1.00PL/IA Y N 0 0 1

3 Changing General Ledger

Numbers of Overdue Credits 1 1 1 1 44 M 1.00PL/IA Y N 0 0 1

4 Calculation of Effective

Interest Rate of Retail Credits 1 2 2 1 622 N 3.67PL/IA Y N 1 0 1

5 Calculating Adjustment Amount of

Retail Credits 1 2 2 1 620 N 3.33PL/IA Y N 1 0 1

6 Daily Adjustment Accounting 1 5 1 1 818 N 2.25PL/IA Y N 0 0 0

Changing Commission and

Income General Numbers of

Amortization Batch

1 1 0 1 34 M 1.33PL/IA Y N 0 0 0

Transfer of Opening Commissions

related to Commercial Credits with

payment plan number to the

income system

1 1 1 1 44 M 1.00Cool:

Gen A N Y 0 0 0

Transfer of Opening Commissions

related to Commercial Credits with

no payment plan number to the

income system (PL/I batch is used)

1 1 0 1 33 M 1.00PL/IA Y N 0 0 0

Cancel amortization of opening

commissions related to commercial

credits with payment plan number

to the income system

1 1 1 1 44 M 1.00Cool:

Gen A N Y 0 0 0

Transfer of Periodic Commissions

related to Commercial Credits with

payment plan number to the

income system

1 1 0 1 34 M 1.33Cool:

Gen A N Y 0 0 0

Cancel amortization of periodic

commissions related to

commercial credits with payment

plan number to the income system

1 1 1 1 44 M 1.00PL/IA Y N 0 0 0

Transfer of Opening Commissions

related to Retail Credits with

payment plan number to the

income system

1 1 1 1 44 M 1.00Cool:

Gen A N Y 0 0 0

Cancel amortization of opening

commissions related to retail

credits with payment plan number

to the income system

1 1 1 1 44 M 1.00Cool:

Gen A N Y 0 0 0

Transfer of Periodic Commissions

related to Retail Credits with

payment plan to the income system

1 1 1 1 44 M 1.00PL/IA Y N 0 0 0

Cancel amortization of periodic

commissions related to commercial

retail income system

1 1 1 1 44 M 1.00PL/IA Y N 0 0 0

17 Commercial Credits Commission

Rediscount Create 1 1 1 1 42 M 0.50PL/IA Y N 0 0 0

18 Commercial Credits Commission

Rediscount Function 2 2 2 1 710 N 1.43PL/IA Y N 1 0 0

Calling IFRS general link to

calculate EIR, Adjustment and

Effective amount in Commercial

Credits Investment Rediscount

1 2 2 1 610 N 1.67Cool:

Gen A N Y 1 0 0

Calling IFRS general link to

calculate EIR, Adjustment and

Effective amount in Retail Credits

Investment Rediscount

1 2 2 1 610 N 1.67Cool:

Gen A N Y 1 0 0

Building Productivity Models for Small Enhancements

128

No Identification E R W X CFPEffortTypeRatioTool QualityBatch Online Write ExitModule

Calling IFRS general link to

calculate EIR, Adjustment and

Effective amount in Retail

Credits Commission Rediscount

1 2 2 1 6 8 N 1.33Cool:GenA N Y 1 0 0

Cancel of calling amortization

create from periodic commission

collection of retail credits

1 1 1 1 4 3 M 0.75PL/I A Y N 0 0 0

Cancel of calling amortization

create from opening commission

collection of retail credits

1 1 1 1 4 3 M 0.75Cool:GenA N Y 0 0 0

Cancel of calling amortization

create from periodic commission

collection of commercial credits

1 1 1 1 4 3 M 0.75PL/I A Y N 0 0 0

Calling income system link from

periodic commission collection

of retail credits

1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

Calling income system link from

opening commission collection

of retail credits

1 1 1 1 4 4 M 1.00Cool:GenA N Y 0 0 0

Calling income system link from

periodic commission collection

of commercial credit

1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

28 Cancel of calling amortization

create from accrual system 1 1 1 1 4 3 M 0.75PL/I A Y N 0 0 0

Calling commercial credits

commission rediscount create

link from accrual system

1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

Connection between income

system and amortization system

for opening commission records

of commercial credits with no

payment plan

1 0 1 1 3 4 M 1.33Cool:GenA N Y 0 0 1

31 Extra Commission Collection

Facility for Commercial Credits1 6 5 1 13 35 N 2.69Cool:GenA N Y 1 0 1

32 Extra Commission Collection

Facility for Retail Credits 1 5 4 1 11 28 N 2.55Cool:GenA N Y 1 0 1

Discarding new Transactions

from the First Level of

Accounting Unload

1 1 1 3 1 M 0.33PL/I A Y N 0 0 0

Adding new Transactions to the

Second Level of Accounting

Unload

1 1 1 3 1 M 0.33PL/I A Y N 0 0 0

Loading accounting from excel

file to the system without using

general parametric accounting

link

1 3 1 1 6 14 N 2.33PL/I A Y N 0 0 1

Comparison of accounting

movement and account

movement to control trial

balance (in two ways: from

accounting to account movement

and from account movement to

accounting movement)

1 2 1 4 12 M 3,00PL/I A Y N 0 0 1

37 Facility to insert general numbers

according to product 1 3 1 1 6 10 N 1.67Cool:GenA N Y 0 0 0

Facility to insert old product

codes and new products codes

into product change parameter

table

1 2 1 1 5 6 M 1.20Cool:GenA N Y 0 0 0

39 NBR Opening Commission

Report of Retail Credits 1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

40 NBR Periodic Commission

Report of Retail Credits 1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

41 NBR Extra Commission Report

of Retail Credits 1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

42 NBR Opening Commission

Report of Commercial Credits 1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

Building Productivity Models for Small Enhancements 129

No Identification E R W X CFPEffortTypeRatio Tool QualityBatch Online Write ExitModule

43 NBR Periodic Commission

Report of Commercial Credits1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

44 NBR Extra Commission Report

of Commercial Credits 1 1 1 1 4 4 M 1.00PL/I A Y N 0 0 0

45 Facility to Relate Income Codes

with Product Codes 1 1 1 1 4 4 M 1.00Cool:GenA N Y 0 0 0

46 List of Income Codes Related to

Product Codes 1 1 1 1 4 8 M 2.0 Cool:GenA N Y 0 0 0

47 Impair Flag Update for Risky

Credits 1 5 2 1 9 16 N 1.78PL/I A Y N 1 0 1

48 Calculating Effective Interest

Amount of Commercial Credits1 6 2 1 1026 N 2.60PL/I A Y N 1 0 1

Calculation Effective Interest

Rate(EIR) of Commercial

Credits

1 2 2 1 6 12 N 2.00PL/I A Y N 1 0 1

50 Calculating Adjustment Amount

of Commercial Credits 1 2 2 1 6 13 N 2.17PL/I A Y N 1 0 1

51 Customer Account Report

General Ledger Number Change1 1 1 3 1 M 0.33PL/I A Y N 0 0 0

52 Recover Table Lock Escalation

Problem 1 1 1 1 4 10 M 2.50PL/I A Y N 0 0 0

53 Income Delivery Operations 1 4 3 2 1040 N 4.00PL/I A Y N 1 0 1

54 Pricing Service List 1 1 2 4 16 M 4.00Cool:GenA N Y 0 0 0

55 Service Definition Operation 1 2 1 3 7 16 N 2.29Cool:GenA N Y 0 1 0

56 Record Priority Screen 1 1 1 1 4 8 M 2.00Cool:GenA N Y 0 0 0

57 Pricing Service code Product

Code Relation 1 1 1 1 4 8 M 2.00Cool:GenA N Y 0 0 0

58 Service Pricing 3 1 3 1 8 24 N 3.00Cool:GenA N Y 1 0 0

59 Business of Industry Price

Detail Entry 2 3 1 2 8 22 N 2.75Cool:GenA N Y 0 0 0

60 Reference Price Detail Entry 2 3 1 2 8 18 N 2.25Cool:GenA N Y 0 0 0

61 Special Price Detail Entry 2 3 1 2 8 19 N 2.38Cool:GenA N Y 0 0 0

62 Charge Commission Amount

Querying 2 6 4 1238 N 3.17Cool:GenA N Y 0 1 1

63 Calculation of Profitability of

Customer for Company 1 2 2 5 20 M 4.00PL/I A Y N 0 0 1

64 Extract of Account Report 1 3 2 6 24 N 4.00PL/I A Y N 0 0 1

65 Revenue List 1 3 2 6 22 N 3.67PL/I A Y N 0 0 1

66 Campaign Parameter Entry

Screen Change 1 1 1 2 5 8 M 1.60Cool:GenA N Y 0 0 0

67 Closing Accounts According to

Criteria 1 4 1 2 8 24 N 3.00PL/I A Y N 0 0 1

68 General Ledger Number Update1 3 1 2 7 11 N 1.57PL/I A Y N 0 0 0

Adding New Fields to the

Campaign Parameter Entry

Screen

1 1 1 1 4 10 M 2.5 PL/I A Y Y 0 0 0

Adding New Fields to the

Campaign Product Parameter

Entry Screen

1 1 1 3 8 M 2.67PL/I A Y Y 0 0 0

71 Customer Report Interest

Amount Setting 1 2 1 4 6 M 1.5 PL/I A Y N 0 0 0

72 Adding new Accounting Case

To the Income Return Process1 2 1 1 5 10 M 2 Cool:GenA N Y 0 0 1

73 Retail Credits Interest Discount

Control Link 1 1 1 3 8 M 2.67Cool:GenA N Y 0 0 0

74 Customer Transfer Check Link1 1 1 3 8 M 2.67Cool:GenA N Y 0 0 0

75 Cost Matrix Special Price

Definition Log List 1 1 2 4 6 M 1.5 PL/I A Y N 0 0 0

Building Productivity Models for Small Enhancements

130

No Identification E R W X CFPEffortTypeRatioTool Quality Batch Online Write Exit Module

76 Accounting Transaction Group

List Service 1 1 1 3 8 M 2.67Cool:GenA N Y 0 0 0

77 Account Plan Service 1 1 1 3 8 M 2.67Cool:GenA N Y 0 0 0

78 Accounting Transaction List

Performance Enhancement 1 1 1 3 7 M 2.33Cool:GenA N Y 0 0 0

Simulation facility to calculate

EIR, adjustment and effective

amount

1 1 2 4 8 2 Cool:GenA N Y 0 0 0

80 Valuable Fund Tax Transfer 1 3 1 1 6 10 1.67PL/I A Y N 0 0 1

81 General Ledger Number

Update for Unit 1 2 1 4 4 1 PL/I A Y N 0 0 0

82 Credit Read Service 1 1 1 3 8 2.67Cool:GenA N Y 0 0 0

83 Retail Credits Master

Information Read Service 1 1 1 3 8 2.67Cool:GenA N Y 0 0 0

84 Parametric Accounting Detail

Read Service 1 2 2 5 10 2 Cool:GenA N Y 0 0 0

85 Rediscount Information Update

Service 1 1 1 1 4 8 2 Cool:GenA N Y 0 0 0

86 Amortization Information

Update Service 1 1 1 1 4 8 2 Cool:GenA N Y 0 0 0

87 Commission Report new fields

request for Retail Credits 1 1 2 4 3 0.75PL/I A Y N 0 0 0

88 Commission Report new fields

request for Commercial Credits1 2 2 5 5 1 PL/I A Y N 0 0 0

Appendix B. Small Enhancements Documentation (2 Examples)

1) Changing General Ledger Numbers of Retail Credits

There is a ledger number for each product code. If the product code is changed, ledger number must be changed.

Trigger: Product code change

Entry: Product Code

Read: General Ledger (1 Read)

Write: General Ledger (1Write)

Error Message: 1 Exit (General Ledger number is not defined related to product code)

1(E) + 1(R) + 1(W) + 1(X) = 4CFP

Quality: A

Effort Enh. = 4 hours

Data Group: General Ledger

2) Changing General Ledger Numbers of Commercial Credits

There is a ledger number for each product code. If the product code is changed, ledger number must be changed.

Trigger: Product code change

Entry: Product Code

Read: General Ledger (1 Read)

Write: General Ledger (1Write)

Error Message: 1 Exit (General Ledger number is not defined related to product code)

1(E) + 1(R) + 1(W) + 1(X) = 4CFP

Quality: A

Effort Enh. = 4 hours

Data Group: General Ledger