1 software quality cis 375 bruce r. maxim um-dearborn

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Software Quality

CIS 375

Bruce R. Maxim

UM-Dearborn

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Software Quality Principles

• Conformance to software requirements is the foundation from which quality is measured.

• Specified standards define a set of development criteria that guide the manner in which software is engineered.

• Software quality is suspect when a software product conforms to its explicitly stated requirements and fails to conform to the customer's implicit requirements (e.g. ease of use).

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McCall’s Quality Factors

• Product Operation– Correctness– Efficiency– Integrity– Reliability– Usability

• Product Revision– Flexibility– Maintainability– Testability

• Product Transition– Interoperability– Portability– Reusability

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McCall’s Quality Factors

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McCall’s Software Metrics

• Auditability• Accuracy• Communication

commonality• Completeness• Consistency• Data commonality• Error tolerance• Execution efficiency• Expandability• Generality

• Hardware independence• Instrumentation• Modularity• Operability• Security• Self-documentation• Simplicity• Software system

independence• Traceability• Training

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FURPS Quality Factors

• Functionality

• Usability

• Reliability

• Performance

• Supportability

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ISO 9126 Quality Factors

• Functionality

• Reliability

• Usability

• Efficiency

• Maintainability

• Portability

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Measurement Process - 1

• Formulation– derivation of software measures and metrics

appropriate for software representation being considered

• Collection– mechanism used to accumulate the date used to

derive the software metrics

• Analysis– computation of metrics

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Measurement Process - 2

• Interpretation– evaluation of metrics that results in gaining

insight into quality of the work product

• Feedback– recommendations derived from

interpretation of the metrics is transmitted to the software development team

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Technical Metric Formulation

• The objectives of measurement should be established before collecting any data.

• Each metric is defined in an unambiguous manner.

• Metrics should be based on a theory that is valid for the application domain.

• Metrics should be tailored to accommodate specific products and processes

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Software Metric Attributes

• Simple and computable

• Empirically and intuitively persuasive

• Consistent and objective

• Consistent in use of units and measures

• Programming language independent

• Provides an effective mechanism for quality feedback

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Representative Analysis Metrics

• Function-based metrics

• Bang metric– function strong or data strong

• Davis specification quality metrics

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Specification Quality Metrics - 1

nn = nf + nnf

nn = requirements & specification .

nf = functional .

nnf = non-functional .

Specificity, Q1 = nai/qr

nai = # of requirements with reviewer

agreement.

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Specification Quality Metrics - 2

Completeness, Q2 = nu / (ni * ns)

nu = unique functions.

ni = # of inputs.

ns = # of states.

Overall completeness, Q3 = nc / (nc + nnv)

nc = # validated & correct.

nnv = # not validated.

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Representative Design Metrics - 1

• Architectural design metrics– Structural complexity (based on module fanout)– Data complexity (based on module interface inputs

and outputs)– System complexity (sum of structural and data

complexity)– Morphology (number of nodes and arcs in

program graph) – Design structure quality index (DSQI)

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Representative Design Metrics - 2

• Component-level design metrics– Cohesion metrics (data slice, data tokens,

glue tokens, superglue tokens, stickiness)– Coupling metrics (data and control flow,

global, environmental)– Complexity metrics (e.g. cyclomatic

complexity)

• Interface design metrics (e.g. layout appropriateness)

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Halstead’s Software ScienceSource Code Metrics

• Overall program length• Potential minimum algorithm volume • Actual algorithm volume

– number of bits used to specify program

• Program level– software complexity

• Language level– constant for given language

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Testing Metrics

• Metrics that predict the likely number of tests required during various testing phases

• Metrics that focus on test coverage for a given component

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Estimating Number of ErrorsError Seeding - 1

(s / S) = (n / N)

S = # of seeded errors

s = # seeded errors found

N = # of actual errors

n = # of actual errors found so far

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Estimating Number of ErrorsError Seeding – 2

E(1) = (x / n) =

(# of real errors found by 1/ total # of real errors) =

q / y =

(# errors found by both / # real errors found by 2)

E(2) = (y / n) = q / x =

(# real errors found by both / # found by 1)

n = q/(E(1) * E(2))

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Estimating Number of ErrorsError Seeding – 3

Assumex = 25

y = 30

q = 15

E(1) = (15 / 30) = .5

E(2) = (15 / 25) = .6

n = [15 / (.5)(.6)] = 50 errors

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Software Confidence

S = # of seeded errors.

N = # of actual errors.

C (confidence level) = 1 if n > N

C (confidence level) =

[S / (S – N + 1)] if n <= N

Example: N = 0 and S = 10C = 10/(10 – 0 + 1) = 10/11 91%

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Confidence Example

How many seeded errors need to be used and found to have a 90% confidence a program is bug free?N = 0

C = S/(S – 0 + 1) = 98/100

Solving for SS = 49

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Failure Intensity

• Suppose that intensity is proportional to # of faults or errors present at the start of testing.– function A has 90% of duty time– function B has 10% of duty time

• Suppose there are 100 total errors50 in A, 50 in B(.9)50K + (.1)50K = 50K(.1)50K = 5K

or (.9)50k = 45K

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Maintenance MetricsSoftware Maturity Index

SMI = [Mt = (Fa + Fc + Fd)]/Mt

Mt = number of modules in current release.

Fa = modules added.

Fc = modules changed.

Fd = modules deleted.

• SMI approaches 1.0 as product begins to stabilize