Quality Assurance by Means of Feature Models David Gollasch FOSD - - PowerPoint PPT Presentation

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Faculty of Computer Science , Institute of Software - and Multimedia - Technology, Chair for Software Technology Quality Assurance by Means of Feature Models David Gollasch FOSD Meeting 2014, Dagstuhl, 07 .05.2014 Contents 1 2 3 Quality

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FOSD Meeting 2014, Dagstuhl, 07 .05.2014

Quality Assurance by Means of Feature Models

Faculty of Computer Science, Institute of Software- and Multimedia-Technology, Chair for Software Technology

David Gollasch

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Quality Assurance by Means of Feature Models FOSD Meeting, 07 .05.2014

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Contents

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1 Fundamentals

SaaS Quality Assurance

2 Quality Assurance by Means of Feature Models

Quality as Attributes in Feature Models Structural Analysis of Feature Models

3 Discussion

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Motivation

3

Modern business applications are getting increasingly distributed over the Internet as multi-tenant software as a service (SaaS). This leads to new challenges in terms of quality assurance when developing or maintaining such applications, because all customers are directly affected very often. Which effects do software changes have? How are those changes proactively determinable? How useful are feature models to face this?

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  • 1. Fundamentals

4 SaaS Quality Assurance

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Multi-Tenant Software as a Service Applications

5 SaaS Instance

Tenant Tenant Tenant Config. Config. Config.

Configurations possible through product lines (SPL)

[Mietzner et al. 2011] [Schroeter et al. 2012] Domain Engineering Application Engineering

…can be seen as a special kind of SPL

[Linden 2007 , p. 6 ff.]

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Why to focus on quality assurance?

  • 1. Law of continous change
  • 4. Law of diminishing productivity
  • 2. Law of increasing complexity
  • 5. Law of restricted growth
  • 3. Law of decreasing quality

6 [Sneed 2012, EVOL-21]

Laws of software evolution (development of software in time) key argument for quality assurance

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How to assure quality in general?

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Take a quality goal and try to reach it due to further development Prioritize development process: Remove unnecessary features to avoid wasting time and money. X G Q exclusive gradual quantized analyse product structure

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  • 2. Quality Assurance by Means of Feature Models

8 Quality as Attributes in Feature Models Structural Analysis of Feature Models

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Qualities as Attributes in Feature Models

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  • Steps to analyze quality goals:

Classification as X, G

  • r Q goal

Find metric or heuristics Consolidation for configuration 1. 2. 3.

Type Quality Goal Question/Interpretation X goal Capabilities Are the requirements fulfilled? G goal Efficiency How efficient is the feature or configuration? Q goal Resource usage How much memory needs the calculation?

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Example: Comparing Configurations (Summation as Consolidation)

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„Measurement“

  • n feature

Consolidation 300 < 500 300 200 100 200 100 200 300 300 500

Configuration 1 Configuration 2

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Consolidation Methods

  • simple approach for X goals: binary (achieved/not achieved)


ex: If there is one sub feature which does not achieve the X goal, the whole configuration does not achieve the goal.

!

  • simple arithmetic operations for Q/G goals: e.g. summation

!

  • complex consolidation method with dependencies, because not every

feature set allows a simple summation of the quality values, 
 e.g. in terms of memory consumption.
 If FA and FB → multiply sum with 0.5.

11

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Further Measurement Approaches

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  • Not every quality goal can be measured easily, e.g. safety properties!

!

  • Quality measurement at a concrete software instance respectively

configuration

!

  • usage of Benchmarks
  • usage of model and code

!

  • Quality determination by means of a business approach
  • Assigning „costs“ to each feature
  • Negotiation of „total costs“ for a configuration according to economical

principles (discounts, price increase, ...)

[Siegmund et al. 2012] [Lettner et al. 2011]

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Structural Similarity of Configurations

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  • When does comparing configurations make sense?
  • Comparison of similar configurations as a lead for further investigations

same parent feature Configurations are similar, if there are commonalities that can be identified. This does not necessarily refer to the selection of equal features. Similarity also involves structural commonalities. (derived definition)

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Structural Similarity of Configurations

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  • When does comparing configurations make sense?
  • Comparison of similar configurations as a lead for further investigations

same parent feature same group Configurations are similar, if there are commonalities that can be identified. This does not necessarily refer to the selection of equal features. Similarity also involves structural commonalities. (derived definition)

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Structural Similarity of Configurations

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  • When does comparing configurations make sense?
  • Comparison of similar configurations as a lead for further investigations

same parent feature same group same features of a group Configurations are similar, if there are commonalities that can be identified. This does not necessarily refer to the selection of equal features. Similarity also involves structural commonalities. (derived definition)

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Structural Similarity of Configurations

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  • When does comparing configurations make sense?
  • Comparison of similar configurations as a lead for further investigations

same parent feature same group same features of a group same features of a parent feature Configurations are similar, if there are commonalities that can be identified. This does not necessarily refer to the selection of equal features. Similarity also involves structural commonalities. (derived definition)

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Structural Similarity of Configurations

13

  • When does comparing configurations make sense?
  • Comparison of similar configurations as a lead for further investigations

same parent feature same group same features of a group same features of a parent feature same attributes Configurations are similar, if there are commonalities that can be identified. This does not necessarily refer to the selection of equal features. Similarity also involves structural commonalities. (derived definition)

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Draw Conclusions (1)

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Quality change due to software evolution

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Draw Conclusions (2)

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Quality difference of similar configurations …and its change due to software evolution

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Structural Analysis of Feature Models

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  • Comparisons only based
  • n a feature model and

existing derived configurations.

  • Complete automation

possible

!

  • Conceptual

implementation in Java as Eclipse plugin within the extFM-Tooling project (https://github.com/ extFM/extFM-Tooling/)

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  • 3. Discussion
  • Possible starting points for further research

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Analysis of the shown methods without the SaaS context Further research in attributed feature models Extension of analysis tools Integration of the prototypical implementation in a practically usable tool Combination of attributed and structural analysis Empirical investigation in practicability in real projects

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FOSD Meeting 2014, Dagstuhl, 07 .05.2014

Thank you for your attention!

Faculty of Computer Science, Institute of Software- and Multimedia-Technology, Chair for Software Technology

1 Fundamentals

SaaS Quality Assurance

2 Quality Assurance by Means of Feature Models

Quality as Attributes in Feature Models Structural Analysis of Feature Models

3 Discussion (References on next slides)

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References (1)

19

  • Illustrationen von www.freedigitalphotos.net
  • [Buxmann et al. 2008] Peter Buxmann, Thomas Hess und Sonja Lehmann. ”Software as a Service“

. In: Wirtschaftsinformatik 50.6 (2008), S. 500–503.

  • [Blaisdell 2012] Rick Blaisdell. Mandantenfähigkeit in der Cloud: Die Vorteile verstehen. Blog. Juli 2012.

url: http://www.enterprisecioforum.com/de/blogs/rickblaisdell/mandantenf%C3%A4higkeit-der-cloud-die- vorteil (besucht am 06.07 .2013).

  • [Böckle et al. 2004] Günter Böckle, Peter Knauber und Klaus Pohl. Software-Produktlinien: Methoden,

Einführung und Praxis. Hrsg. von Klaus Schmid. Heidelberg: Dpunkt.verlag, 2004.

  • [Kang et al. 1990] K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak und A. S. Peterson. Feature-Oriented

Domain Analysis (FODA) Feasibility Study. Techn. Ber. Carnegie-Mellon University Software Engineering Institute, Nov. 1990.

  • [Linden 2007] Frank van der Linden. Software Product Lines in Action: The Best Industrial Practice in

Product Line Engineering. Berlin, New York: Springer, 2007 .

  • [Benavides et al. 2009] David Benavides, Sergio Segura und Antonio Ruiz-Cortés. Automated Analysis
  • f Feature Models: A Detailed Literature Review. Techn. Ber. ISA-09-TR-04. Seville, Spain: Applied

Software Engineering Research Group, University of Seville, 2009.

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References (2)

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  • [Benavides et al. 2005] David Benavides, Pablo Trinidad und Antonio Ruiz-Cortés. ”

Automated Reasoning

  • n Feature Models“

. In: LNCS, Advanced Information Systems Engineering: 17th International Conference, CAISE 2005. Springer, 2005.

  • [Sneed 2012] Harry Sneed. Software Product Management. Blockvorlesung. Technische Universität

Dresden, Fakultät Informatik, Okt. 2012.

  • [VÖRBY 2013] VÖRBY

. Datei:ISO 9126 Grafik.png. Page Version ID: 113618779. Juli 2013. url: http:// de.wikipedia.org/w/index.php?title=Datei:ISO 9126Grafik.png&oldid=113618779 (besucht am 02. 08. 2013).

  • [Balzert 1998] Helmut Balzert. Lehrbuch der Software-Technik-2 : Software-Management, Software-

Qualitätssicherung, Unternehmensmodellierung. Heidelberg, Berlin: Spektrum Akad. Verl., 1998.

  • [Mohmood, Hill 2011] Zaigham Mahmood und Richard Hill. Cloud Computing for Enterprise
  • Architectures. Computer Communications and Networks. London, Dordrecht, Heidelberg, New York:

Springer, 2011.

  • [Gumzej, Halang 2010] Roman Gumzej und Wolfgang A. Halang. Real-time Systems’ Quality of Service.

London, Dordrecht, Heidelberg, New York: Springer, 2010.

  • [Siegmund et al. 2012] Norbert Siegmund, Marko Rosenmüller, Martin Kuhlemann, Christian Kästner,

Sven Apel und Gunter Saake. ”SPL Conqueror: Toward optimization of non-functional properties in software product lines“ . In: Software Quality Journal 20.3-4 (2012), S. 487–517 .

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References (3)

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  • [Mietzner et al. 2011] Ralph Mietzner , Frank Leymann & Tobias Unger (2011) Horizontal and vertical

combination of multi-tenancy patterns in service-oriented applications, Enterprise Information Systems, 5:1, 59-77

  • [Schroeter et al. 2012] Julia Schroeter, Peter Mucha, Marcel Muth, Kay Jugel, and Malte Lochau.

Dynamic Configuration Management of Cloud-based Applications. In: Proceedings of the 16th International Software Product Line Conference (SPLC'12), Volume 2, SCArVeS Workshop, ACM, September 2012

  • [Lettner et al. 2011] Daniela Lettner, Daniel Thaller, Michael Vierhauser, Rick Rabiser, Paul Grünbacher,

Wolfgang Heider: Supporting business calculations in a product line engineering tool suite. SPLC Workshops 2011: 26

  • [Roos-Frantz et al. 2011] Fabricia Roos-Frantz, David Benavides, Antonio Ruiz-Cortés, André Heuer, Kim
  • Lauenroth. Quality-aware analysis in product line engineering with the orthogonal variability model.

Springer Science+Business Media, LLC 2011