(Some) Software Engineering Research at NJIT

1. (Some) Software Engineering Research at NJIT Sergio Bogazzi, Yao Fei Chen, GuanJie Jiang, BoYu, Ali Mili

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

3. Software Architecture Metrics A Study of Change and Error Propagations

4. Project Objective • 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

5. Research Plan • Define and validate computable metrics for software architectures based on information theoretic measures • 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

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

7. What are these Metrics useful for? • 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

8. What are these Metrics useful for?(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

9. 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

10. Premises of Our Approach • 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

11. Recent Accomplishments • 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

12. Information Coupling and Cohesion Metrics • 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

13. S A B X OR x x` x` x Definition of Quantitative Factors: Error Propagation Error • 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

14. Static Error Propagation Matrix SEPM (Flattened architecture of a NASA case study) Error Propagation Coefficient (static) = 0.0503 Professor Ali Mili

15. Dynamic Error Propagation Matrix DEPM (Flattened architecture of a NASA case study) Error Propagation Coefficient (dynamic) = 0.0534 Professor Ali Mili

16. Fault Model Components, Connectors Extract Model Info. Design Experiments UML Model Connector, Message SummitBASIC Corrupt Model Simulateand Log Normal Log Analyze Logs Empirical Error Propagation MATLAB Error Logs Future WorkEmpirical Validation of Error Propagation Analysis Professor Ali Mili

17. Change Propagation • 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

18. Analyzing Change Propagation: Simple Model • Model architectural components as input-output transformers C: IC OC, from the set of inputs (IC ) of the component C to the set of its outputs (OC ). • 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

19. Empirical work • 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

20. The experiment is still undergoing Professor Ali Mili

21. Automated Tool • 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

22. Benefits • 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

23. Future Work • 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

24. Verification and Validation of Online Adaptive Systems • Jian GuanJie, Ali Mili, NJIT • Bojan Cukic, Yan Liu, WVU Professor Ali Mili

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

26. Traditional V&V Techniques • Fault Avoidance • Fault Removal • Fault Tolerance • All are inapplicable to adaptive learning systems Professor Ali Mili

27. Refinement-based V&V of adaptive systems • Functional Envelope • Monotonic Learning • Safe Learning Professor Ali Mili

28. H F R X Rh Y X Fh Y Functional Envelope Professor Ali Mili

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

30. Monotonic Learning----MLP Professor Ali Mili

31. Monotonic Learning----DCS Professor Ali Mili

32. Safe Learning Professor Ali Mili

33. Novelty Detection Professor Ali Mili

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

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

36. 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

37. Possible Factors Which Affect Trends • Intrinsic factorsare 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

38. Extrinsic factorsare 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

39. 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

40. I1 F (I1, I2, …, In) O1 I2 Factors that might affect the trend O2 Factors that can be used to evaluate the trend I3 Om In Function used to describe the relationship among factors. Models Construction&Validation • Use statistics method to construct&validate Models Professor Ali Mili

41. 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

42. Analyzing Redundancy Ali Mili, NJIT Bojan Cukic, WVU Jules Desharnais, Laval University

43. Outgrowth of Dryden Project • 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

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

45. Non Surjectivity 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

46. Duplication of State Information 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

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