Software Productivity Measurement Using Multiple Size Measures

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Apr 19, 2006 - 2/20. Contents. ○Introduction. ○Background. ○Related work. ○Motivation. ○Productivity measurement. - Measurement model. - Productivity ...
Software Productivity Measurement Using Multiple Size Measures Barbara Kitchenham and Emila Mendes IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2004

2006-04-19 Kim, Su-hyun

Contents |Introduction |Background |Related work |Motivation |Productivity measurement - Measurement model - Productivity measure construction - Productivity analysis

|Conclusion |Discussion Software Engineering Lab, KAIST

2/20

Introduction |Productivity - Simple ratio of product size to project effort - Broadly used to benchmarking - Difficult to measure ƒ No standard model for aggregating these measure Productivity =

Amount of output Unit of input used

Software Engineering Lab, KAIST

3/20

Background |Research trend - Common assumptions ƒ Size must be related to effort ƒ Reused components should not be included

- Productivity measure method ƒ Used single size measures • FP, SLOC, System Meter, Magnitude, Use Case Point • Standard size/effort ratio

ƒ Approach on multidimensional size measure

Software Engineering Lab, KAIST

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Related work (1/4) |Reifer’s work [IEEE SOFTWARE, 2000] - Estimating quick-to-market software - Size of web application ƒ Using Halstead’s equation V* = Nlog2(n) = (N1* + N2*) log2 (n1* + n2*)

distinct number

Volume Volume total number Software Engineering Lab, KAIST

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Related work (2/4) |Reifer’s work (cont’d) - Web based predictors of size

Software Engineering Lab, KAIST

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Related work (3/4) |Stensrud and Myrtveit’s work [TSE’03] - Identifying high performance ERP projects ƒ Productivity is an indicator ƒ The smallest project is always the most productive

- Relative measure ƒ Variable returns to scale (VRS) P=

y x

x=

1 P

y

B

P: productivity x: effort y: FP or SLOC B > 1 (COCOMO)

Software Engineering Lab, KAIST

Effort = a × Size b 7/20

Related work (4/4) |Stensrud and Myrtveit’s work (cont’d) increasing return to scale (small project) decreasing return to scale (large project) constant return to scale (medium project)

Software Engineering Lab, KAIST

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Motivation |Mendes’s work [METRICS’03] - Early web size measures and effort prediction for web costimation (133 companies) ƒ From Tukutuku database Company-specific dataset Multi-company dataset

Case-based reasoning

best?

Stepwise regression

Software Engineering Lab, KAIST

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Measurement model (1/3) |Considerations - Finding elements related to efforts Web Web pages pages

High High effort effort functions functions

New New images images

- Combinations of size measures ƒ Multiplicative parameters (non-linear)

- Obtaining the total effort ƒ Person-hours • No information about the proportion of total effort spent constructing new elements and the proportion of total effort spent on reused elements

Software Engineering Lab, KAIST

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Measurement model (2/3) |Assumptions - Effort is related to several different size measure - Size-based effort estimation model ƒ The expected value of productivity is one ƒ A value greater than one is indicative of good productivity (Expected effort)

Software Engineering Lab, KAIST

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Measurement model (3/3) |Assumptions (cont’d) - Economies and diseconomies of scale should be account for

b>1

diseconomies of scale

size↑ productivity↓

Large size

b