Sergio Bogazzi, Yao Fei
Chen, GuanJie Jiang,
BoYu, Ali Mili

Software Architecture Analysis
Verification and Validation of Online
Adaptive Systems
Software Engineering Trends
Analyzing Redundancy
Professor Ali Mili 2

A Study of Change and
Error Propagations

Defining and Investigating Quality
Metrics for Software Architectures.
Applying these Metrics to Large Scale
Software Architectures.
Correlate the Metrics with Independent
Observations of Quality.
Professor Ali Mili 4

Define and validate theoretic measures computable metrics for software architectures based on information
Automate the process of computing these metrics.
Apply the process and metrics to a NASA case study.
Validate the correctness and usefulness of these metrics.
Professor Ali Mili 5

Error Propagation. Significance for reliability.
Change Propagation. Significance for
Maintainability.
Requirements Propagation. Signifi- cance for Evolvability.
Professor Ali Mili 6

By looking at the Error Propagation Metrics the architect would be able to identify components that have high potential to propagate errors through the system,
,
The error propagation matrices, are also very useful in direction of test plans, critical components that show high tendency to propagate error to other system components should be more thoroughly tested.
Professor Ali Mili 7

(cont’d)
By looking at the Change Propagation Metrics
(CPM) , the architect would be able to identify components that have high potential to propagate the changes inside them to other components.
From CPM, the architect would also be able to identify components that have high potential to be affected by the changes in other components
Professor Ali Mili 8

Project Overview
Define Information theoretic metrics
Coupling, cohesion, redundancy
Define quantitative factors that are relevant to qualitative attributes of the architecture
Error Propagation
Change Propagation
Establishing analytical and/or empirical relationship between the metrics and the quantitative factors
Automate computation of the metrics
Professor Ali Mili 9

Architectural Level precludes a logic, semantic-based approach; hence we use a stochastic approach.
Information Theory has a wide range of functions that quantify random variables.
We use analytical and empirical means to correlate metrics to qualitative properties.
Professor Ali Mili 10

Further Analysis of the Correlation between Error Propagation and
Computable Metrics.
Initiating the Analysis of Change
Propagation in the Context of State
Based Components.
Automation of the derivation of metrics from architectural descriptions (in UML, by Nicholay) and from source code (by
Sergio).
Professor Ali Mili 11

We treat coupling in the true architectural sense, as a property of the connector between components.
Coupling in our approach becomes a function of an ordered pair of components characterizing their informational interdependence.
Cohesion of a component measures its internal information flow.
Professor Ali Mili 12

Definition of Quantitative Factors:
Error Propagation
A
S
B
X x` x
OR x`
Error x

The error propagation (EP) is a measure of the likelihood that an error in the message sent by A will propagate into B .
Professor Ali Mili 13

Static Error Propagation Matrix SEPM
(Flattened architecture of a NASA case study)
C4
C5
C6
C7
C1
C2
C3
C8
C9
C10
B
C1 C2 C3 C4 C5 C6 C7
0.18
1 0.11
0.38
0.44
0.58
0.59
1 0 0 0 0
0.11
0.26
1 0 0 0
0.17
0.15
0 0.57
0.23
0 0.18
0
0.08
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
C8 C9 C10
0
0
0 0.5
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
Error Propagation Coefficient (static) = 0.0503
Professor Ali Mili 14

Dynamic Error Propagation Matrix DEPM
(Flattened architecture of a NASA case study)
C7
C8
C9
C10
C4
C5
C6
C1
C2
C3
B
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1 0.11
0.15
0.3
0.76
0.73
0 0 0
0.37
0.03
0.32
1 0
1
0
0
0
0
0
0 0
0 0.52
0
0
0
0
0
0
0.03
0.19
0 0.67
0 0.28
0.32
0
0
0 0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
Error Propagation Coefficient (dynamic) = 0.0534
Professor Ali Mili 15

Empirical Validation of Error Propagation
Analysis
UML Model Extract
Model Info.
Simulate and Log
Components,
Connectors
Design
Experiments
Connector,
Message
Corrupt
Model
Fault
Model
Summit
BASIC
Error Logs
Normal Log
Analyze
Logs
Empirical Error Propagation
MATLAB
Professor Ali Mili 16

We define Change Propagation from component A to component B as the probability that a change in A due to corrective/ defective maintenance requires a change in B to maintain the overall function of the system.
We are conducting empirical experiment and trying to propose an analytical formula that approximate change propagation
Professor Ali Mili 17

Analyzing Change Propagation: Simple Model
Model architectural components as input-output transformers
C : I
C
 O
C
, from the set of inputs ( to the set of its outputs ( O
C
).
I
C
) of the component C
Consider elementary two-component (pipeline?) architecture, where the first component A “feeds” its output to the second component B as its input. The functionality of the architecture can be presented in the form of the following diagram:
Professor Ali Mili 18

In our experiment, we randomly select a large amount of changes within each component, then go into the source code level to see, if the change will propagate to other components.
By computing for each pair of components (A,
B), the number for which change in A cause a change in B, we are able to derive the change propagation matrix.
Professor Ali Mili 19

C9
C10
C11
C12
C13
C14
C15
C16
C5
C6
C7
C8
C1
C2
C3
C4
C17
C18
Sum
C1
1
C2 C3 C4
1/11 2/11 1/11
1
1/5 1
1
C5
1
1/3
1/8
4/8
1/3
1/8 4/8
1/3
2/5
1/3
1/3
2/2
4/5
1/1
C6
1/11
1
1/8
1/3
Change Propagation Matrix
C7 C8
1
1/3
2/8
2/5
1/3
1
1/8
1/1
C9
2/11
2/3
C10 C11 C12 C13 C14 C15 C16 C17 C18 Changes
1/11 1/11 3/11 11
3
4/5
2/2
0/2
1/3
5
2
1
3
3/8
2/3
1 1/8
1
1/3
2/8
6/8
1
1
1
1
1/3
2
0
3
0
8
8
8
3
1/3
2/5
1
1 1/5
1
1/1
1
1
3
1
5
67
The experiment is still undergoing
Professor Ali Mili 20

Simplify application of the theory and methods developed to industrial-strength systems
Give project manager an ability to get a glimpse into the quality of software architecture  Project Manager View
Configure the tool for handling specific
ADLs and CASE tools  Analyst View
Professor Ali Mili 21

The developments of techniques for measuring error/change propagation help analysts identify trouble spots in the architecture (advances the state of the art)
The development of automated tools help the analyst apply these techniques on large architectures (advances the state of the practice)
Professor Ali Mili 22

Use a tool such as SIAT TM , or Understand TM to measure static information theoretic metrics
Compare the measures obtained to those obtained at the design level
Compare information coupling and cohesion measures with traditional measures based on
McCabe.
Perfect the tools
Professor Ali Mili 23

Jian GuanJie, Ali Mili, NJIT
Bojan Cukic, Yan Liu, WVU
Professor Ali Mili 24

The Intelligent Flight Control System (IFCS)
IFCS Project: NASA DFRC; NASA ARC;
Boeing; ISR; WVU.
Professor Ali Mili 25

Fault Avoidance
Fault Removal
Fault Tolerance
All are inapplicable to adaptive learning systems
Professor Ali Mili 26

Professor Ali Mili 27

H
F R
X
X R h
F h
Y
R h

  
F h

Professor Ali Mili
Y
28

Initial Weights

0
=1.0

0
=0.5

0
=0.0

0

[-0.3, 0.3]
Input
(1,1)
(1,0)
(0,1)
(0,0)
(1,1)
(1,0)
(0,1)
(0,0)
(1,1)
(1,0)
(0,1)
(0,0)
(1,1)
(1,0)
(0,1)
(0,0)
10
0.95718
0.88929
0.88985
0.74329
0.88763
0.75727
0.76131
0.58170
0.70029
0.60074
0.60987
0.55051
0.50565
0.48353
0.49364
0.51421
0.51080
0.48304
0.49480
0.51010
20
0.58756
0.51103
0.52137
0.49504
0.50740
0.48525
0.49367
0.51434
0.51098
0.48416
0.49397
0.51027
Iteration Times with Output
50
0.64715
0.49946
0.50934
0.41602
0.53496
0.48290
0.48944
0.48686
0.50880
0.48714
0.49203
0.51342
0.51101
0.48627
0.49197
0.51051
100
0.57213
0.48526
0.48949
0.45580
0.52377
0.48596
0.48869
0.49964
0.50976
0.48836
0.49037
0.51227
0.51096
0.48794
0.49027
0.51073
500
0.50373
0.51578
0.51579
0.50102
0.12861
0.88881
0.88868
0.09960
0.51087
0.49385
0.49424
0.51760
0.51116
0.48878
0.48888
0.51134
0.51118
0.48876
0.48885
0.51137
2000
0.14863
0.87160
0.87141
0.11487
0.50880
0.49985
0.50006
0.51613
0.50909
0.49888
0.49911
0.51662
Converge
7285
0.04999388
0.95669980
0.95675480
0.03909299
7560
0.04999296
0.95670090
0.95675580
0.03909090
8926
0.04999402
0.95669870
0.95675415
0.03909125
8942
0.04999226
0.95670134
0.95675653
0.03908928
Professor Ali Mili 29

Professor Ali Mili 30

Professor Ali Mili 31

 h
 
, F h

( R h
Professor Ali Mili
S )
32

Professor Ali Mili 33

Experimental Results of Novelty Detection Using Association Rule
Learning
Professor Ali Mili 34

Programming Language Trends:
An Empirical Study
Yao Fei Chen, Ali Mili
New Jersey Institute of Technology

Introduction
As part of the project of monitoring software engineering technical trends, we are trying to research the evolution of high level programming language.
What are the possible factors which can affect programming language trends?
How can we quantify these factors?
How can we watch/predict/adapt to/affect the trends based the factors?
Professor Ali Mili 36

Possible Factors Which Affect Trends
Intrinsic factors are the factors which can be used to describe the general design criteria of programming language.
Generality
Orthogonality
Reliability
Maintainability
Efficiency
Simplicity
Implementability
Machine Independence
Extensibility
Expressiveness
Influence/Impact
Professor Ali Mili 37

Extrinsic factors are the factors which are not directly related to the general attributes of a programming language, but still can affect the trend of programming language.
Institutional Support
Industrial Support
Governmental Support
Organizational Support
Grassroots Support
Technology Support
Professor Ali Mili 38

Quantifying Factors
Quantifying intrinsic factors:
All intrinsic factors should be considered when one designs a programming language. So, we will try to go over all features of a programming language to check if it matches these factors. Then, we will assign a score to each factor for each programming language.
Quantifying extrinsic factors:
We will do survey for every extrinsic factor and assign scores for them.
Professor Ali Mili 39

Models Construction&Validation

Use statistics method to construct&validate
Models
Factors that might affect the trend
I1
I2
I3
In
F (I
1
, I
2
, …, I n
)
O
1
O
2
O m
Factors that can be used to evaluate the trend
Function used to describe the relationship among factors.
Professor Ali Mili 40

Evaluating Programming Languages

By analyzing a set of programming language, we expect that we can find out statistics models and use them to predict the future trend of a programming language.
















COBOL
FORTRAN
LISP
ALGOL
PASCAL
C
C++
JAVA
ADA
ML
APL
MODULA
EIFFEL
PROLOG
SMALLTALK
SCHEME
Professor Ali Mili 41

Ali Mili, NJIT
Bojan Cukic, WVU
Jules Desharnais, Laval University

What is Redundancy?
Does Redundancy characterize a state or its representation?
Is all redundancy amenable to state redundancy?
Can we talk about redundancy without Fault
Tolerance?
Instant Redundancy vs Temporal Redundancy
Professor Ali Mili 43

Non-Injectivity of Representation Function
Duplication of State information
Scale of Fault Tolerant Capabilities.
Professor Ali Mili 44

Representation function from States to
Representations
Total (representability): integers.
Injective (precision): reals.
Surjective (Non-redundancy)
No representation functions satisfy all. Most functions satisfy none.
Professor Ali Mili 45

Axiomatization of functions quantifying duplication
Zero for redundancy free state
One for simple duplication for redundancy free state.
N-1 for N-modular redundancy
Continuous real values in-between.
Professor Ali Mili 46

Levels of Correctness:
Correctness, Maskability, Recoverability,
Non-recoverability.
Recovery is necessary and sufficient:
Recoverability.
Sufficient conditions of recoverability.
Professor Ali Mili 47