Web Productivity Measurement and Benchmarking

1. Department of S/W Engineering
University : Malakand pakistan
Subject : Web Engineering

2. Definition:
It is the relationship between the output
generated from a system and the inputs that
are used to create that output.
 It is measured as ratio of output to input
 More output per unit of input, more
productive will the project.
 In software engineering the output is taken
as product size and input as effort

3.  So the equation for Productivity is;
Productivity = Size/Effort
 Size means the Line of codes, functions of the
project
 Effort means the cost, Human experts, tools
and other resources.
 Productivity Baseline is “1”
Greater than “1” is high and good productivity
Less than “1” is low and poor productivity.

4.  There is no standard for aggregating all size
measures of a web application into a single
size measure.
So it is difficult to measure web application
and also its productivity.

5.  It allows the use of different size measures.
 It has built-in interpretation scale(extracting
meaning of something or significance).
 It uses multiple effort-related size measures
and size-based effort measures which is a
function of effort-related size measure.
 As the aggregate size measure is difficult so
size-based effort measure model is defined
as “Adjusted size” measure.

6.  So the Productivity is given as;
Productivity = Adjusted size/Effort
 Adjusted size measures contains only size
measures that together are strongly Related
with effort.

7.  To increase the S/w Product Productivity.
 To increase the S/w product quality.
 To develop more valuable products for lower
costs.
 To utilize resources fairly.
 To manage the project in best way.

8.  S/w products(statements, functions, external
components, Documents etc).
 S/w production Processes and structures
(Requirements analysis,
Specification{operations, relations}
Architectural Design,
Coding, and Testing).
 S/w Production settings (Programming
languages, Computing Platform, s/w
development Enviroment etc).

9.  Programmers
 Team managers
 Projects Managers
 Outside Analysts
 Outside Observers

10.  Get best and well managed people
(developers).
 Make development steps efficient and more
effective.
 Simplify, collapse or eliminate development
steps.
 Build simple products or product families.

11.  It is the act of running set of programs or
operations in order to know the relative
performance of an object, normally by
running standard tests.
 It provides a method of comparing the
performance of the various subsystems.

12.  Use to analysis Productivity of web app from
Tukutuku Database.
 This Database has data of 87 web Projects.
Two main steps of case study:
1. Build Productivity Measure using
Productivity Measurement Method.
2. Use of Productivity Values (Upper and Lower
Bound)

13. Conclusion:
Four issues were investigated during
Productivity Analysis;
1. The impact of reuse of web pages on
productivity.
2. The impact of Team size on Productivity
3. The impact of number of Programming
Languages on Productivity.
4. The impact of average team experience with
Programming language on Productivity.

14. Result:
The following result is eliminated from
above Case Study;
1. The reuse of Web pages have no impact on
Productivity.
2. The Team size, number of programming
languages and average team experience
with Programming language have impact on
Productivity.