-By: Aadarsh Sharma-152409-ME CSE (Regular)

IntroductionAttributes of Software MetricsActivities of a Measurement ProcessTypes Normalization of MetricsReferences

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Help software engineers to better understand the attributes of models and assess the quality of the software

Help software engineers to gain insight into the design and construction of the software

Focus on specific attributes of software engineering work products resulting from analysis, design, coding, and testing

Provide a systematic way to assess quality based on a set of clearly defined rules

Provide an “on-the-spot” rather than “after-the-fact” insight into the software development

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Measure Provides a quantitative indication of the extent, amount,

dimension, capacity, or size of some attribute of a product or process

Metric (IEEE) A quantitative measure of the degree to which a

system, component, or process possesses a given attribute Indicator

A metric or combination of metrics that provides insight into the software process, a software project, or the product itself

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To indicate the quality of the product. To assess the productivity of the

people who produce the product To assess the benefits derived from

new software engineering methods and tools

To form a baseline for estimation To help justify requests for new tools

or additional training

Formulation The derivation (i.e., identification) of software measures and

metrics appropriate for the representation of the software that is being considered

Collection The mechanism used to accumulate data required to derive

the formulated metrics Analysis

The computation of metrics and the application of mathematical tools

Interpretation The evaluation of metrics in an effort to gain insight into the

quality of the representation Feedback

Recommendations derived from the interpretation of product metrics and passed on to the software development team

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Simple and computable It should be relatively easy to learn how to derive the metric,

and its computation should not demand inordinate effort or time

Empirically and intuitively persuasive The metric should satisfy the engineer’s intuitive notions about

the product attribute under consideration Consistent and objective

The metric should always yield results that are unambiguous

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(More on next slide)

Consistent in the use of units and dimensions The mathematical computation of the metric should use

measures that do not lead to bizarre combinations of units Programming language independent

Metrics should be based on the analysis model, the design model, or the structure of the program itself

An effective mechanism for high-quality feedback The metric should lead to a higher-quality end product

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1. Process Metrics

2. Product Metrics

3. Project Metrics

Process metrics are measures of the software development process, such as Overall development time Type of methodology used

Process metrics are collected across all projects and over long periods of time.

Their intent is to provide indicators that lead to long-term software process improvement.

Project Metrics are the measures of Software Project and are used to monitor and control the project. They enable a software project manager to: Minimize the development time by making the

adjustments necessary to avoid delays and potential problems and risks.

Assess product quality on an ongoing basis & modify the technical approach to improve quality.

Product metrics are measures of the software product at any stage of its development, from requirements to installed system. Product metrics may measure: the complexity of the software design the size of the final program the number of pages of documentation

produced

Direct measures Easy to collect E.g. Cost, Effort, Lines of codes (LOC),

Execution Speed, Memory size, Defects etc. Indirect measures

More difficult to assess & can be measured indirectly only.

Quality, Functionality, Complexity, Reliability, Efficiency, Maintainability etc.

2 different project teams are working to record errors in a software process

Team A – Finds 342 errors during software process before release

Team B- Finds 184 errors

Which team is more effective in finding errors?

To answer this we need to know the size & complexity of the projects.

But if we normalize the measures, it is possible to compare the two

For normalization we have 2 ways- Size-Oriented Metrics Function Oriented Metrics

• Based on the “size” of the software produced

Project Effort (person-month)

Cost ($)

LOC kLOC Doc. (pgs)

Errors

People

A 24 168,000 12100 12.1 365 29 3

B 62 440,000 27200 27.2 1224 86 5

Errors per KLOC $ per KLOC Pages of documentation per KLOC Errors per person-month LOC per person-month

Advantages of Size Oriented Metrics LOC can be easily counted Many software estimation models use LOC or KLOC as input.

Disadvantages of Size Oriented Metrics LOC measures are language dependent, programmer

dependent Their use in estimation requires a lot of detail which can be

difficult to achieve.

The function point metric (FP), first proposed by Albrecht, can be used effectively as a means for measuring the functionality delivered by a system.

Functionality is measured indirectly using a measure called function point.

Function points (FP) - derived using an empirical relationship based on countable measures of software & assessments of software complexity

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Information Domain Value Count simple average complex

Weighting factor

External Inputs (EIs)External Outputs (EOs)

External Inquiries (EQs)Internal Logical Files (ILFs)

External Interface Files (EIFs)

3 4 6

4 5 7

3 4 6

7 10 155 7 10

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Count total

33333

• Errors per FP• Defects per FP• $ per FP• Pages of documentation per FP

1. Count the measurement parameters.2. Assess the complexity of the values.3. Calculate the raw FP (see next table).4. Rate the complexity factors to produce

the complexity adjustment value (CAV)5. Calculate the adjusted FP as follows:

FP = raw FP x [0.65 + 0.01 x CAV]

• Advantages: language independent, based on data known early in project, good for estimation

• Disadvantages: calculation complexity, subjective assessments, FP has no physical meaning (just a number)

http://www.slideshare.net/13-software-metrics

Software Engineering: A Practitioner’s Approach, 6th edition by Roger S. Pressman

https://en.wikipedia.org/wiki/Software_metric

http://www.cs.kent.edu/~jmaletic/cs63901/lectures/SoftwareMetrics.pdf

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Any Queries ?

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