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An Overview of Software Code Quality and Connection to Business Process Quality Yann-Gal Guhneuc 1 st IEEE International Workshop on Communicating Business Process and Software Models (Quality, Understandability, and Maintainability)

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Yann-Gaël Guéhéneuc

This work is licensed under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported License

An Overview of Software Code Quality and Connection to Business Process Quality

1st IEEE International Workshop on Communicating Business Process and Software Models

(Quality, Understandability, and Maintainability)

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Outline

 Example  Introduction  Trail  Definitions  Following the Trail  Conclusion

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Introduction

 Software total cost of ownership (TCO)

includes all the costs to acquire and keep a program running, from specifications to licenses to training

 Maintenance contributes between 50% and

90% of TCO [Erlikh, Lientz]

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Introduction

 Four factors impact maintainers [ISO9126]

– Software analysability

  • Their ability to identify and understand defects

– Software Changeability

  • The effort needed to change the software

– Software Stability

  • The sensitivity of the software to change

– Software Testability

  • The effort needed to test the software
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Introduction

 Two factors impact software analysability

– Software quality characteristics

  • ISO/IEC standards 9126:2001 and 25000:2005

decompose software quality into process quality, product quality, and quality in use

– Software products are the output of software processes. Product quality is determined by the degree to which the developed software meets the defined requirements

– Developers’ characteristics

  • Status, experience, education, gender
  • …
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Introduction

 Three dimensions characterise software

quality characteristics

– Functional vs. non-functional

  • At runtime vs. overall

– Internal vs. external

  • Maintainability vs. understandability

– Metric-based vs. practice-based

  • Objective vs. subjective
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Introduction

 Three dimensions characterise software

quality characteristics

– Functional vs. non-functional

  • At runtime vs. overall

– Internal vs. external

  • Maintainability vs. understandability

– Metric-based vs. practice-based

  • Objective vs. subjective
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Outline

 Example  Introduction  Trail  Definitions  Following the Trail  Conclusion

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Trail

 In this talk, we will discuss how to measure

the overall quality characteristics of maintainability and understandability

  • bjectively and subjectively
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Trail

Maintainability Understandability Quality Models Models Good Practices Definition Metrics Detection Occurrences Social Studies Characteristics Eye-tracking Studies Behaviour Factors Measures

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Outline

 Example  Introduction  Trail  Definitions  Following the Trail  Conclusion

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Definitions

 Maintainability

– Ease with which a software system can be modified [IEEE Standard Glossary of Software Engineering Terminology]

 Understandability

– Ease with which a software system can be understood [Boehm]

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Definitions

 Software code artefacts

– Source code, such as Java – UML-like models – …

 Labelled, weighted multi-graphs

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Definitions

 Business process artefacts

– Languages, such as BEPL – BPMN – …

 Labelled, weighted multi-graphs

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Outline

 Example  Introduction  Trail  Definitions  Following the Trail  Conclusion

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Trail

Maintainability Understandability Quality Models Models Good Practices Definition Metrics Detection Occurrences Social Studies Characteristics Eye-tracking Studies Behaviour Factors Measures

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Quality Models

Problem: how to measure software code artefacts? Solution: define and use quality metrics and models

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Quality Models

 Quality models

– A model with the objective to describe, assess and–or predict quality [Deissenboeck]

 Metrics  Models

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Quality Models

 Metrics have been well researched since the

early days of software engineering

– Chidamber and Kemerer – Hitz and Montazeri – Lorenz and Kidd – McCabe – …

(Don’t miss Briand et al.’s surveys on the metrics of cohesion and coupling)

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Quality Models

 Metrics alone are

meaningless

– Size vs. IQ – … – Hotness vs. IQ [Srivastava]

http://hardsci.wordpress.com/2013/09/17/the-hotness-iq-tradeoff-in-academia/

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Quality Models

 Few quality models exist

– Boehm’s – ISO9126 – McCall’s – QMOOD – …

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Quality Models

 Bansiya and Davis’ QMOOD

– Hierarchical model for OO designs – Structural and behavioural design properties of classes, objects, and their relationships

  • Reusability, flexibility, and complexity

– Object-oriented design metrics, such as

  • Encapsulation, modularity, coupling, and cohesion

– Validated using empirical and expert opinion on several large commercial systems

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Quality Models

 Difficulty to relate metric values with quality

characteristics, such as maintainability and understandability

 Difficulty to identify relevant metrics and

show that the metrics measure what they are expected to measure

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Quality Models

 Metrics

– 3QM-Framework [Overhage et al.] – ProM [Vanderfeesten et al.]

 Models

There probably exist more?

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Trail

Maintainability Understandability Quality Models Models Good Practices Definition Metrics Detection Occurrences Social Studies Characteristics Eye-tracking Studies Behaviour Factors Measures

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Good Practices

Problem: how to identify good/bad practices? Solution: collect and detect models of the practices

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Good Practices

 Software engineering development and

maintenance are based on a few principles

– SOLID – Do not reinvent the wheel – Beware of assumptions – …

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Good Practices

 Martin and Feather’s SOLID

– Single responsibility – Open/closed – Liskov substitution – Interface segregation – Dependency inversion

(Don’t miss Michael Feather’s keynote on Thursday on the software useful life)

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Good Practices

Each pattern describes a problem which

  • ccurs over and over again in our environ-

ment, and then describes the core of the solution to that problem, in such way that you can use this solution a million times

  • ver, without ever doing it the same way

twice. —Christopher Alexander, 1977

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Good Practices

Important assumptions

– That patterns can be codified in such a way that they can be shared between different designers. – That reuse will lead to “better” designs. There is an obvious question here of what constitutes “better”, but a key measure is maintainability.

—Zhang and Budgen, 2012 (With minor adaptations)

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Good Practices

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Good Practices

 Design Patterns

– A general reusable solution to a commonly

  • ccurring problem within a given context in

software design

 Design Antipatterns

– A design pattern that may be commonly used but is ineffective/counterproductive in practice

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Good Practices

 Pattern solution = Motif which

connotes an elegant architecture

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Good Practices

How to identify in the architecture

  • f a program

micro-architectures similar to design motifs to explain the problem solved?

Figure CompositeFigure AttributeFigure PolyLineFigure DecoratorFigure

To compose objects in a tree-like structure to describe whole–part hierarchies

Frame DrawingEditor Tool Handle Panel DrawingView Drawing Figure AbstractFigure CompositeFigure AttributeFigure PolyLineFigure DecoratorFigure Component
  • peration()
Leaf
  • peration()
Composite add(Component) remove(Component) getComponent(int)
  • peration()
ramification For each components component.operation() 1..n Client
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Good Practices

 What motifs and what model for these

motifs?

 What model for the program architecture?  How to perform the identification?

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Good Practices

 What motifs and what model for these

motifs?

 What model for the program architecture?  How to perform the identification?

Design Meta-model Design motifs and a motif meta-model

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Good Practices

 Multi-layer framework for design motif

identification

 Information retrieval

– Search space – Query – Results

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Good Practices

 Multi-layer framework

for design motif identification

Code Model Model + Associations, aggregations Model + Associations, aggregations, and composition

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Good Practices

 Constraint satisfaction problem solved with

explanation-based constraint programming (e-CP) to obtain

– No a priori descriptions of variations – Justification of the identified micro-architectures – Strong interaction with the developers

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Good Practices – Example

 Design motif ( )

Component

  • peration()

Leaf

  • peration()

Composite

add(Component) remove(Component) getComponent(int)

  • peration()

ramification

For each components component.operation()

1..n Client

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Good Practices – Example

 Example of JHotDraw

– Erich Gamma and Thomas Eggenschwiler – 2D drawing – Design patterns

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Good Practices – Example

Frame DrawingEditor Tool Handle Panel DrawingView Drawing Figure AbstractFigure CompositeFigure AttributeFigure PolyLineFigure DecoratorFigure

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Good Practices – Example

 Micro-architecture ( )  Maintainability  Understandability

Figure CompositeFigure AttributeFigure PolyLineFigure DecoratorFigure

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Component

  • peration()

Leaf

  • peration()

Composite

add(Component) remove(Component) getComponent(int)

  • peration()

ramification

For each components component.operation()

1..n Client

Frame DrawingEditor Tool Handle Panel DrawingView Drawing Figure AbstractFigure CompositeFigure AttributeFigure PolyLineFigure DecoratorFigure

e-CP

V = {component, leaf, composite} C = {leaf < component, composite < component, composite component} D = {DrawingEditor, DrawingView…}

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Frame DrawingEditor Tool Handle Panel DrawingView Drawing Figure AbstractFigure CompositeFigure AttributeFigure PolyLineFigure DecoratorFigure

e-CP

V = {component, leaf, composite} C = {leaf < component, composite < component, composite component} D = {DrawingEditor, DrawingView…}

< < < <

Component

  • peration()

Leaf

  • peration()

Composite

add(Component) remove(Component) getComponent(int)

  • peration()

ramification

For each components component.operation()

1..n Client

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Good Practices

 Search space can

be very large and the efficiency in time of the search very low

 Use metrics and

topology to reduce the search space

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Good Practices

 “Design” patterns

– Workflow patterns [van der Aalst]

 “Design” antipatterns

– In BPEL [Palma et al.]

More? Detection? Impact?

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Trail

Maintainability Understandability Quality Models Models Good Practices Definition Metrics Detection Occurrences Social Studies Characteristics Eye-tracking Studies Behaviour Factors Measures

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Social Studies

Problem: how to characterise developers? Solution: study developers’ social behaviours

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Social Studies

 Developers’ characteristics

– Gender

  • Males vs. females

– Professional status

  • Practitioner vs. Students

– Expertise

  • Experts vs. novices

(Don’t miss the sessions Developers, What are you Thinking? and Context)

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Social Studies

 Such studies are typically experiments

– Independent variable (few) – Dependent variables (many) – Statistical analyses – Threats to validity

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Social Studies

 For example, impact on identifiers on

program understandability

– Identifier styles [Sharif, Binkley] – Identifier quality [Lawrie] – Developers’ gender and identifiers [Sharafi] – …

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Social Studies

 Independent variables

– Gender: male vs. female – Identifier: camel case vs. underscore

 Dependent variables

– Accuracy – Time – Effort

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Social Studies

 Subjects  Conclusions

Subjects’ Demography (24 Subjects)

Academic background Gender

Ph.D. M.Sc. B.Sc. Male Female

11 10 3 15 9

Accuracy Time Effort

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Social Studies

 Threats to validity

– Construct validity

  • Measurements represent the construct being studied

– Internal validity

  • A causal conclusion based on the study is warranted

– External validity

  • Extent to which the results of the study can be

generalised to other contexts or developers

– Conclusion validity

  • Whether the presumed cause and effect covary
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Social Studies

 Few studies

– Notations [Recker, Reijers]

  • Understandability
  • Personal factors
  • Model factors

– Structuredness [Dumas et al.]

  • Understanding

– Others?

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Trail

Maintainability Understandability Quality Models Models Good Practices Definition Metrics Detection Occurrences Social Studies Characteristics Eye-tracking Studies Behaviour Factors Measures

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Eye-tracking Studies

Problem: how to study developers’ behaviour? Solution: study developers’ thought processes

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Eye-tracking Studies

 Developers’ thought processes

– Cognitive theories

  • Brooks’
  • Von Mayrhauser’s
  • Pennington’s
  • Soloway’s

– Mental models

  • Gentner and Stevens’ mental models

– Memory theories

  • Kelly’s categories
  • Minsky’s frames
  • Piaget’s schema
  • Schank’s scripts
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Eye-tracking Studies

 Studying developers’

thought processes

– Yarbus’ eye-movements and vision – Just and Carpenter’s eye-mind hypothesis – Palmer’s vision science – …

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Eye-tracking Studies

 Picking into developers’ thought processes

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Eye-tracking Studies

 Picking into developers’ thought processes

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Eye-tracking Studies

 Such studies are typically experiments

– Independent variable (few) – Dependent variables (many) – Statistical analyses – Threats to validity

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Eye-tracking Studies

 For example, impact of design pattern

notations

– Strongly visual [Schauer and Keler] – Strongly textual [Dong et al.] – Mixed [Vlissides] – …

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Eye-tracking Studies

 Independent variables

– Design pattern notations – Tasks: Participation, Composition, Role

 Dependent variables

– Average fixation duration – Ratio of fixations – Ration of fixation times

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Eye-tracking Studies

 Subjects

– 24 Ph.D. and M.Sc. students

 Conclusions

– Stereotype-enhanced UML diagram [Dong et al.] more efficient for Composition and Role – UML collaboration notation and the pattern- enhanced class diagrams more efficient for Participation

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Eye-tracking Studies

 Threats to validity

– Construct validity – Internal validity – External validity – Conclusion validity

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Eye-tracking Studies

 One study

– Relevant regions [Petrusel and Mendling]

  • Fixations
  • Fixation time

– Others?

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Outline

 Example  Introduction  Trail  Definitions  Following the Trail  Conclusion

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Conclusion

 How to measure the overall quality

characteristics of maintainability and understandability objectively and subjectively?

 Focus on source code artefacts modelled as

graphs, business process can be modelled as graphs too

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Conclusion

 Software code quality is based on the

modelling of code as graphs and on

– Quality metrics – Quality models – Good practices

and depends

– Experiences – Human factors

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Conclusion

 Business process quality could also be

based on the modelling of business process code as graphs and follow similar research directions as source code?

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Maintainability Understandability Quality Models Models Good Practices Definition Metrics Detection Occurrences Social Studies Characteristics Eye-tracking Studies Behaviour Factors Measures