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Towards a Framework for Pattern Experimentation Towards a Framework for Pattern Experimentation

Towards a Framework for Pattern Experimentation - PowerPoint Presentation

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Towards a Framework for Pattern Experimentation - PPT Presentation

Understanding empirical validity in requirements engineering patterns Travis D Breaux Hanan Hibshi Ashwini Rao Carnegie Mellon University JeanMichel Lehker University of Texas at San Antonio ID: 803743

data requirements patterns pattern requirements data pattern patterns categorization level retention validity cognition engineering international 1978 segmentation triggers outcomes

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Slide1

Towards a Framework for Pattern ExperimentationUnderstanding empirical validity in requirements engineering patternsTravis D. Breaux, Hanan Hibshi, Ashwini RaoCarnegie Mellon UniversityJean-Michel LehkerUniversity of Texas at San Antonio

Second International Workshop on Requirements Patterns (RePa’12)

24 September 2012, Chicago, USA

In conjunction with 20

th

IEEE International Requirements Engineering Conference

Slide2

Slide3

SP 800-53 Catalog of Security Controls15408:2005 Common CriteriaFunctional Requirements603 ASecurity of Personal InformationHIPAA

SECURITY REQUIREMENTS

Slide4

PCI - Data Security Standard3.1.1 Implement data retention and disposalpolicy that includes:Limiting data storage Processes for secure deletion of dataSpecific retention requirements…Identifying requirements is difficult

Slide5

Pattern Name: Retention and Disposal PatternPattern Activation:Pattern Triggers:Pattern Outcomes:Patterns provide better cues

Slide6

Pattern Name: Retention and Disposal PatternPattern Activation: Data is received, stored or processedPattern Triggers:Data is no longer neededDigital access to the media will changePattern

Outcomes

: Retain data; Dispose data

Patterns improve comprehension

Slide7

Pattern Name: Retention and Disposal PatternPattern Activation: Data is received, stored or processedPattern Triggers:The data is no longer neededDigital access to the media will changePattern Outcomes: Retain data; Dispose dataMandatory Extension Points:When was the data acquired?

What laws, regulations or business requirements exist to retain the data?

Patterns capture variability

Slide8

0101010011001010000000011000000001000100149162536496481

Sequence of

s

quares of

n

umbers 1 to 9

Not all patterns are equal

Slide9

Do you want to empirically know why patterns work?Do you want to trust me that these patterns work?

Slide10

What is pattern application?Requirements analyst shouldRecognize goalRecognize cues in problem descriptionApply patternSatisfy output constraints

Slide11

What is pattern validity?InputApplyOutput

Probability

of selecting the right pattern

Probability

of correct output

Slide12

Requirements Pattern TaxonomyGoalsRepresentationsSources

Slide13

How to evaluate goal satisfaction?We identified 5 goals to improve…Requirements acquisitionRequirements qualityComplianceRequirements engineering processRuntime performance

Slide14

Sources influence outcomesRequirements knowledge can be highly or lightly structuredStructure affects individual interpretationLightly structured  more variation Highly structured  less variation

Slide15

Source/Representation (Mis)match

Slide16

Cognitive Psychology TheoriesHow do humans learn?How do humans interact with abstractions?

Slide17

Does cognition affect application?

Slide18

What features of input description increase or decrease validity?Segmentation (Vertical)Level of Inclusiveness (Horizontal)Category

A

B

C

D

Basic Level

Figure developed from E.

Rosch

, “Principles of Categorization,”

Cognition and Categorization

, pp.

27-48, 1978.

Slide19

What features of input description increase or decrease validity?Segmentation (Vertical)Level of Inclusiveness (Horizontal)Category

A

B

C

D

Basic Level

Figure developed from E.

Rosch

, “Principles of Categorization,”

Cognition and Categorization

, pp. 27-48, 1978.

Slide20

What features of input description increase or decrease validity?Segmentation (Vertical)Level of Inclusiveness (Horizontal)Category

A

B

C

D

Basic Level

Figure developed from E.

Rosch

, “Principles of Categorization,”

Cognition and Categorization

, pp. 27-48, 1978.

Slide21

Ongoing WorkDiving deeper into cognitive psychologyDesigning experiments for pilot studiesExtending literature review of our requirements pattern taxonomy

Slide22

AcknowledgementThis presentation is based on the Pecha Kucha template available athttp://www.conferencesthatwork.com/index.php/presentations/2011/09/tips-for-organizing-pecha-kucha-sessions/Second International Workshop on Requirements Patterns (RePa’12)24 September 2012, Chicago, USAIn conjunction with 20th IEEE International Requirements Engineering Conference