An Infrastructure for Generating Run-time Model Traces for Maintenance Tasks

Current research efforts are focused on taking advantage of the models at run-time for run-time decisionmaking related to run-time system concerns associated with autonomic and adaptive systems. In addition, all systems need maintenance over time as new requirements emerge or when bug-fixing becomes necessary. Models at run-time can provide an execution trace of a high level of abstraction that is useful for maintenance tasks. In this paper, we propose a generic infrastructure, which is able to get the run-time model trace. Our infrastructure creates a descriptive model of the running code by means of Code-Model Connection Rules. These rules translate the behaviour of the running source code in model traces. We validate our infrastructure in a Smart Hotel. The results of our infrastructure show promising results towards the generation of model traces from source code at run-time. However, further work is required to a better specification of the rules, to solve some issues with the model dependencies and to allow the propagation of changes in the models to the source code.

[1]  Javier Muñoz Ferrara.  Model driven development of pervasive systems. Building a software factory , 2008 .

[2]  Oscar Nierstrasz,et al.  Model-Centric, Context-Aware Software Adaptation , 2009, Software Engineering for Self-Adaptive Systems.

[3]  G. Travassos,et al.  Contributions of In Virtuo and In Silico Experiments for the Future of Empirical Studies in Software Engineering , 2003 .

[4]  H. D. Rombach,et al.  The Goal Question Metric Approach , 1994 .

[5]  H. William Dettmer,et al.  Goldratt's Theory of Constraints: A Systems Approach to Continuous Improvement , 1997 .

[6]  David Sinreich,et al.  An architectural blueprint for autonomic computing , 2006 .

[7]  Carlos Cetina Englada.  Achieving autonomic computing through the use of variability models at run-time , 2010 .

[8]  Martin Gogolla,et al.  OCL-based Runtime Monitoring of JVM hosted Applications , 2011, Electron. Commun. Eur. Assoc. Softw. Sci. Technol..

[9]  Meir M. Lehman,et al.  A Paradigm for the Behavioural Modelling of Software Processes using System Dynamics , 2001 .

[10]  Uwe Zdun,et al.  Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime , 2016, Software & Systems Modeling.

[11]  Bogdan Dit,et al.  Feature location in source code: a taxonomy and survey , 2013, J. Softw. Evol. Process..

[12]  Holger Giese,et al.  Living with Uncertainty in the Age of Runtime Models , 2014, Models@run.time@Dagstuhl.

[13]  Alexei Lisitsa,et al.  Logics in Artificial Intelligence, 10th European Conference, JELIA 2006, Liverpool, UK, September 13-15, 2006, Proceedings , 2006, JELIA.

[14]  Nelly Bencomo,et al.  Models@run.time (Dagstuhl Seminar 11481) , 2011, Dagstuhl Reports.

[15]  Victor R. Basili,et al.  The role of experimentation in software engineering: past, current, and future , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.

[16]  Per Runeson,et al.  Guidelines for conducting and reporting case study research in software engineering , 2009, Empirical Software Engineering.

[17]  José Júlio Alferes,et al.  An Event-Condition-Action Logic Programming Language , 2006, JELIA.

[18]  Nelly Bencomo,et al.  Models@run.time , 2014, Lecture Notes in Computer Science.

[19]  Marc Ehrig,et al.  Measuring Similarity between Semantic Business Process Models , 2007, APCCM.

[20]  Uwe Zdun,et al.  Enhancing Root Cause Analysis with Runtime Models and Interactive Visualizations , 2013, MoDELS@Run.time.

[21]  Manfred Reichert,et al.  MaDe4IC: an abstract method for managing model dependencies in inter-organizational cooperations , 2010, Service Oriented Computing and Applications.

[22]  Shahar Maoz,et al.  Using Model-Based Traces as Runtime Models , 2009, Computer.

[23]  Klaus D. Müller-Glaser,et al.  Dynamic Mapping of Runtime Information Models for Debugging Embedded Software , 2006, Seventeenth IEEE International Workshop on Rapid System Prototyping (RSP'06).

[24]  D. Marples,et al.  The Open Services Gateway Initiative: an introductory overview , 2001, IEEE Commun. Mag..

[25]  Jeffrey O. Kephart,et al.  An artificial intelligence perspective on autonomic computing policies , 2004, Proceedings. Fifth IEEE International Workshop on Policies for Distributed Systems and Networks, 2004. POLICY 2004..