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Software Architecture Metrics

Software Architecture Metrics. Hany Ammar, Mark Shereshevsky, Ali Mili, Walid Rabie and Nicholay Gradetsky Lane Department of Computer Science & Electrical Engineering hammar@wvu.edu; (smark, amili, rabie, grani)@csee.wvu.edu.

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Software Architecture Metrics

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  1. Software Architecture Metrics Hany Ammar, Mark Shereshevsky, Ali Mili,Walid Rabie and Nicholay Gradetsky Lane Department of Computer Science & Electrical Engineering hammar@wvu.edu; (smark, amili, rabie, grani)@csee.wvu.edu

  2. FY2001 University SOFTWARE INITIATIVE PROPOSALfor theNASA SOFTWARE IV&V FACILITY Initiative Title:Architectural Level Software Metrics '01

  3. Outline • Project Overview • Hub Control Software (HCS) case study • Definition of the entropy based metrics • Definition of the Quantitative Factors • Informational Integrity Coefficient (IIC) • Conclusions (achievements) • Papers submitted • Work In Progress • Future work (2nd year plans)

  4. Project Overview • Developing a class of architectural metrics that quantify information flow in the architecture. Different metrics reflect: • Coupling vs. cohesion; • Data flow vs. control flow; • Static measurement vs. dynamic measurement; • Introducing a group of quantitative factors: mathematically defined functions, that are relevant with respect to qualitative attributes of the architecture • Establishing analytical relationship between the metrics and the quantitative factors • Automating computation of the metrics

  5. Error Propagation, ChangePropagation, Requirements Propagation External Attributes: Maintainability,Testability, Reliability, impacts have Analytical Definitions Quantified Historical Data Analytical Derivations Validation Measurement Values produce Product Artifacts Architecture, Design, Code, computed for Computable Metrics: Coupling, cohesion, complexity, etc. have Mathematical Formula Project Overview • McCall's model of software quality [McCall 1977], • Three-level hierarchy of software attributes

  6. Project Overview McCall's model of software quality in UML

  7. Hub Control Software (HCS) Case StudyInternational Space Station

  8. HCS Other HCS sub-systems N3-1 Data Access N3-2 Data Access RPCM Scheduler ITCS FRITCS State manger PFMC LT SCITCS LRITCS CMD Queue PFMC MT PPA mon O/P CMD Queue General HCS-ITCS scheme

  9. HCS case study: Hierarchical Architecture

  10. S A B CNTL DATA Definition of entropy based metrics • Ensemble • CNTL , DATA  E • p is the probability distribution of E. • VABis a random variable defined by E and p • Definition 1: Coupling: The coupling from A to B, denoted Coupling(A,B), is the entropy of random variable VAB. Cohesion: The cohesion of A is defined as coupling from A to A.

  11. Scitcs Fritcs Pfmc mt pfmc lt lritcs ppa mon Scitcs 3.7 1.58 1.58 1.58 Fritcs 2 3.91 0 0 Pfmc mt 1 2 2 0 pfmc lt 1 2 0 2 lritcs ppa mon HCS case study :ITCS Static Control Matrix

  12. itcs N3 queues rpcm sch state man itcs 1.06 0 1 0 0 0 N3 1 0 0 0 0 0 Queues 0 1 0 0 0 0 rpcm 0 0 0 2 0 0 sch 0 0 0 0 1 0 state man 0 0 0 0 0 1.58 HCS case study :HCS Static Control Matrix (LEVEL 1)

  13. Information Theoretic metrics

  14. Scitcs Fritcs Pfmc mt pfmc lt lritcs ppa mon Scitcs 3.31911948 1 1.5637897 1.19243058 Fritcs 1.53948476 3.65269014 0 0 Pfmc mt 0.73206669 1.46234979 1.9132505 0 pfmc lt 0.80580491 1.48343604 0 1.93555927 lritcs ppa mon HCS case study :ITCS Dynamic Control Matrix

  15. itcs N3 queues rpcm sch state man itcs 1.17573608 0 0.590576 0 0 0 N3 0.988016215 0 0 0 0 0 Queues 0 1 0 0 0 0 rpcm 0 0 0 1.92612075 0 0 sch 0 0 0 0 0.999992 0 state man 0 0 0 0 0 1.584963 HCS case study :HCS Dynamic Control Matrix (LEVEL 1)

  16. Definition of the Quantitative Factors • Quantitative Factors bridge the gap between qualitative attributes (which we want to assess) and computable metrics (which we can measure). • Some Quantitative Factors: • Error Propagation • Change Propagation • Requirements propagation

  17. S A B X OR x x` x` x Definition of the Quantitative Factors: error propagation Error • B(x) is the state of B resulting from invoking B on input x. Suppose, due to a fault in A, the information transmitted from A to B is corrupted from the expected x to some erroneous x`. • The error propagation (EP) is a measure of the likelihood that an error in the message sent by A will propagate into B.

  18. Definition of the Quantitative Factors: error propagation Continued • Definition 2: Error Propagation: The error propagation from A to B is defined as the following conditional probability: EP(A,B) = Prob(B(x) B (x’) | xx’). • This equation can be reduced to: EP(A,B) = 1 – exp( – Coupling(A, B)) + exp( – Cohesion(B)).

  19. Definition of the Quantitative Factors: error propagation Continued • From the analysis of the above equation: • The higher the coupling from A to B – the higher is the error propagation from A to B. • The lower the cohesion of component B – the higher is the error propagation from A to B. • These observations, consistent with intuition, provide an analytical validation of our metrics. Our equation gives these relationships (which are not surprising by themselves) precise analytical form.

  20. Informational Integrity Coefficient (IIC) • One of architecture quality guidelines is to maximize cohesion and minimize coupling of components. • We introduce a quantitative characteristic, which we call the informationalintegrity coefficient of an architecture which assesses its conformance to this guideline. • The coefficient is computed based on the information flow matrix. IIC =

  21. HCS case study :HCS-ITCS (flattened) Static Control Matrix IIC = 0.749

  22. Scitcs Fritcs Pfmc mt pfmc lt lritcs ppa mon Scitcs 3.7 1.58 1.58 1.58 Fritcs 2 3.91 0 0 Pfmc mt 1 2 2 0 pfmc lt 1 2 0 2 lritcs ppa mon HCS case study :ITCS Static Control Matrix IIC = 0.795

  23. itcs N3 queues rpcm sch state man itcs 1.06 0 1 0 0 0 N3 1 0 0 0 0 0 Queues 0 1 0 0 0 0 rpcm 0 0 0 2 0 0 sch 0 0 0 0 1 0 state man 0 0 0 0 0 1.58 HCS case study :HCS Static Control Matrix (LEVEL 1) IIC = 0.861

  24. HCS case study :HCS-ITCS (flattened) Dynamic Control Matrix IIC = 0.773

  25. Scitcs Fritcs Pfmc mt pfmc lt lritcs ppa mon Scitcs 3.31911948 1 1.5637897 1.19243058 Fritcs 1.53948476 3.65269014 0 0 Pfmc mt 0.73206669 1.46234979 1.9132505 0 pfmc lt 0.80580491 1.48343604 0 1.93555927 lritcs ppa mon HCS case study :ITCS Dynamic Control Matrix IIC = 0.884

  26. itcs N3 queues rpcm sch state man itcs 1.17573608 0 0.590576 0 0 0 N3 0.988016215 0 0 0 0 0 Queues 0 1 0 0 0 0 rpcm 0 0 0 1.92612075 0 0 sch 0 0 0 0 0.999992 0 state man 0 0 0 0 0 1.584963 HCS case study :HCS Dynamic Control Matrix (LEVEL 1) IIC = 0.887

  27. HCS case study :IIC ComparisonFlattened Architecture Vs Hierarchical Architecture • The flattened architecture shows a lower IIC than the hierarchical architecture, this proves that the hierarchical architecture is more cohesive and less coupled

  28. Conclusions (achievements) • We defined coupling and cohesion metrics for hierarchical architectures based on the information theory.  • We introduced a normalized measure, the informational integrity coefficient (IIC) of an architecture based on the computed metrics • We derived the relationship between the coupling/cohesion metrics and the error propagation factor between components.

  29. Papers submitted Conferences • Annual International Computer Software and Applications Conference (COMPSAC 2001)Title: "Information-Theoretic Metrics for Software Architectures“ – Status: Accepted for publication • Automated Software Engineering Conference (ASE 2001) Title: "Quantifying Architectural Attributes of UML Specifications: A Framework and Its Automation" – Status: Submitted June 29, 2001 Journals • Automated Software Engineering JournalTitle: "Quantifying Software Architectures: Definitions and Automation Plans“ – Status: submitted: May 11, 2001 • Transactions on Software Engineering JournalTitle: "Information Theoretic Metrics for Software Architectures" – Status: submitted: April, 2001

  30. Future Work • Deriving a relationship between our coupling/cohesion metrics and the change propagation, • Developing an automation methodology for computing the metrics given a UML description of the architecture, • Validating the metrics using a NASA CASE Study

  31. Other views (UML, Rapide) In progress Planned Has a view Automation support for metrics computation Software System Canonical Architectural Style Transformation Conceptual view (RoseRT-UML) Dynamic Metrics Static Metrics Quantitative factors (EP, CP) Information Integrity Coefficient

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