System Development at Run Time

Models are essential for defining and developing systems that support run-time decision-making and reconfiguration, and for implementing autonomous and adaptive systems for remote, hazardous, and largely unknown external environments. We show that they can also be used as the operational code throughout the development process, including deployment. Our ability to build systems with this property depends crucially on Computational Reflection, and our implementation thereof, an integration infrastructure for complex software-intensive systems called Wrappings. It is inherent in a Wrapping system that all activity (down to a specified level of detail) can be recorded as sequences of events with associated context. The system can consider these event elements as points in a “behavior trajectory” space, and use recent advanced mathematical analysis methods to discover hidden relationships in the environment and system behaviors. These relationships can be used to improve the system models and therefore the corresponding behavior.

[1]  Phyllis R. Nelson,et al.  Managing Variable and Cooperative Time Behavior , 2010, 2010 13th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops.

[2]  Bradley R. Schmerl,et al.  Using Architectural Models at Runtime: Research Challenges , 2004, EWSA.

[3]  B. Harrison Las Vegas, Nevada , 2002 .

[4]  Peyman Oreizy,et al.  Architecture-based runtime software evolution , 1998, Proceedings of the 20th International Conference on Software Engineering.

[5]  Christopher J. C. Burges,et al.  Dimension Reduction: A Guided Tour , 2010, Found. Trends Mach. Learn..

[6]  Michele Lanza,et al.  Higher Abstractions for Dynamic Analysis , 2006 .

[7]  Gregor Engels,et al.  Model-Integrating Software Components , 2015, Springer Fachmedien Wiesbaden.

[8]  Dana Angluin,et al.  Finding Patterns Common to a Set of Strings , 1980, J. Comput. Syst. Sci..

[9]  Walter Daelemans Colin de la Higuera: Grammatical inference: learning automata and grammars , 2011, Machine Translation.

[10]  Harold Abelson,et al.  Structure and interpretation of computer programs / Harold Abelson, Gerald Jay Sussman, Julie Sussman , 1985 .

[11]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[12]  Bradley R. Schmerl,et al.  Diagnosing architectural run-time failures , 2013, 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).

[13]  Hong Yan,et al.  Discovering Architectures from Running Systems , 2006, IEEE Transactions on Software Engineering.

[14]  A. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past. Proceedings of the NATO Advanced Research Workshop on a Comparative Time Series Analysis Held in Santa Fe, New Mexico, 14-17 May 1992. , 1994 .

[15]  Bradley R. Schmerl,et al.  Making Self-Adaptation an Engineering Reality , 2005, Self-star Properties in Complex Information Systems.

[16]  Gordon S. Blair,et al.  Genie , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[17]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[18]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[19]  David Garlan,et al.  Stitch: A language for architecture-based self-adaptation , 2012, J. Syst. Softw..

[20]  Olivier Danvy,et al.  Tutorial notes on partial evaluation , 1993, POPL '93.

[21]  Ruben H. Zamar,et al.  Scalable robust covariance and correlation estimates for data mining , 2002, KDD.

[22]  Thomas Vogel,et al.  A language for feedback loops in self-adaptive systems: Executable runtime megamodels , 2012, 2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).

[23]  Christopher Landauer,et al.  Generic programming, partial evaluation, and a new programming paradigm , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[24]  James S. Albus,et al.  Engineering of Mind: An Introduction to the Science of Intelligent Systems , 2001 .

[25]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[26]  Colin de la Higuera,et al.  Grammatical Inference: Learning Automata and Grammars , 2010 .

[27]  Phyllis R. Nelson,et al.  Developing Mechanisms for Determining "Good Enough" in SORT Systems , 2011, 2011 14th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops.

[28]  Roger S. Barga,et al.  Event Correlation and Pattern Detection in CEDR , 2006, EDBT Workshops.

[29]  L. Wasserman Topological Data Analysis , 2016, 1609.08227.

[30]  James Theiler,et al.  Estimating fractal dimension , 1990 .

[31]  Gerald J. Sussman,et al.  Structure and interpretation of computer programs , 1985, Proceedings of the IEEE.

[32]  Christopher Landauer,et al.  Lessons learned from wrapping systems , 1999, Proceedings Fifth IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'99) (Cat. No.PR00434).

[33]  Sahin Albayrak,et al.  Meta-Modeling Runtime Models , 2010, Models@run.time.

[34]  Andrew P. Black,et al.  Breaking the barriers to successful refactoring: observations and tools for extract method , 2008, ICSE.

[35]  Robert Ghrist,et al.  Three examples of applied and computational homology , 2008 .

[36]  Amaury Habrard,et al.  Some improvements of the spectral learning approach for probabilistic grammatical inference , 2014, ICGI.

[37]  Afra Zomorodian,et al.  The tidy set: a minimal simplicial set for computing homology of clique complexes , 2010, SCG.

[38]  Bradley R. Schmerl,et al.  Software Architecture-Based Self-Adaptation , 2009, Autonomic Computing and Networking.

[39]  Christopher Landauer Problem Posing as a System Engineering Paradigm , 2011, 2011 21st International Conference on Systems Engineering.

[40]  Wil M. P. van der Aalst,et al.  Declarative workflows: Balancing between flexibility and support , 2009, Computer Science - Research and Development.

[41]  Christopher Landauer Infrastructure for Studying Infrastructure , 2013, ESOS.

[42]  Christopher Landauer,et al.  Designing Cooperating Self-Improving Systems , 2015, 2015 IEEE International Conference on Autonomic Computing.

[43]  Christopher Landauer,et al.  Refactored Characteristics of Intelligent Computing Systems , 2002 .

[44]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[45]  Alexander M. Meystel,et al.  Intelligent Systems: Architecture, Design, and Control , 2000 .

[46]  Larry Wasserman,et al.  All of Nonparametric Statistics (Springer Texts in Statistics) , 2006 .

[47]  Christopher Landauer,et al.  Self-modeling Systems , 2001, IWSAS.

[48]  Brice Morin,et al.  Models@ Run.time to Support Dynamic Adaptation , 2009, Computer.

[49]  David Garlan,et al.  Reasoning about implicit invocation , 1998, SIGSOFT '98/FSE-6.

[50]  John Hughes,et al.  Lazy Memo-functions , 1985, FPCA.

[51]  Thomas Vogel,et al.  Model-Driven Engineering of Self-Adaptive Software with EUREMA , 2014, ACM Trans. Auton. Adapt. Syst..

[52]  Karan Harbison,et al.  User-Centered Requirements: The Scenario-Based Engineering Process , 1997 .

[53]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[54]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.