ICSE 2011: Q-ImPrESS - An Industrial Case Study on Quality Impact Prediction
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ICSE 2011: Q-ImPrESS - An Industrial Case Study on Quality Impact Prediction
- 1. © ABB Group May 26, 2011 | Slide 1 Q-ImPrESSAn Industrial Case Study on Quality Impact Prediction for Evolving Service-oriented Software Heiko Koziolek, ABB Corporate Research, GermanyBastian Schlich, Carlos Bilich, Roland Weiss, Steffen Becker, Klaus Krogmann, Mircea Trifu, Raffaela Mirandola, Anne Koziolek
- 2. Industrial automation: Process Control Systems © ABB Group May 26, 2011 | Slide 2
- 3. MotivationRelease History for an ABB Process Control System © ABB Group May 26, 2011 | Slide 3 Version A First version release with complete system concept Single environment from independent solutions Outstanding Operations Offering Function based Engineering Redundant Controllers and I/O capabilities Connectivity for Harmony and Melody FF, Redundant Profibus, HART, ABB Drives Version C3 Windows 7 support Alarm Analysis and Alarm Shelving WirelessHART Integration Profinet, Ethernet IP, DeviceNet New Controller PM891 (2x PM866) Engineering efficiency improvements Detailed difference reporting Foundation Fieldbus improvements 2004 2005 2006 2007 2008 2009 2010 Version B Increased system size SIL 2 Integrated Safety Connectivity for DCI and MOD 300 Alarm and Event Improvements Remote Clients via MS Terminal Services Version C1 Increased system size Multi-system Integration SPI Integration (PETI) MODBUS TCP Version C2 Virtualization support MS WPF Graphics SIL3 Safety IEC 61850 (Intel Elect Devices) New PM866 controller (2x PM864) New S800 I/O (non-red HART) New Power Supplies with smaller footprint Evolution Libraries MOD300 and Infi90 Version C Online Upgrade Capability Multi-User / Distributed Engineering Large screen / Multi-screen enhancements Digital Security Improvements
- 4. MotivationProblems of software evolution at ABB Continuous evolution of ABB software systems New requirements, technologies, failure reports Software maintenance and evolution are a large cost factor for ABB software development Current practice Experience to rationalize design decisions Prototyping for new technologies, performance impacts Unknown change impacts on performance/reliability Apply model-based prediction methods for systematic decision support to save costs and achieve higher quality? © ABB Group May 26, 2011 | Slide 4
- 7. Case Study © ABB Group May 26, 2011 | Slide 7
- 8. Manual ModelingSteps executed to create a Q-ImPrESS model © ABB Group May 26, 2011 | Slide 8 ~2.5 person months
- 9. Manual ModelingQ-ImPrESS model of the ABB process control system © ABB Group May 26, 2011 | Slide 9
- 10. Performance PredictionSteps executed to determined resource demands © ABB Group May 26, 2011 | Slide 10 ~1 person month
- 11. Performance PredictionSample predictions for different design alternatives © ABB Group May 26, 2011 | Slide 11
- 12. Performance PredictionResults: Measurements vs. Simulation Results © ABB Group May 26, 2011 | Slide 12
- 13. Reliability PredictionSteps executed to determine failure probabilities © ABB Group May 26, 2011 | Slide 13 ~1 person month
- 14. Each line shows how the system reliability changes if we change onesubsystem reliability (8 subsystems in total) Reliability PredictionSample sensitivity analysis © ABB Group May 26, 2011 | Slide 14
- 15. Reliability PredictionResults © ABB Group May 26, 2011 | Slide 15 More research and tool development needed
- 16. ConclusionsSummary Q-ImPrESS provides a structured method and useful tool support is best used for evolutionary changes, not full redesigns still needs to demonstrate costs/benefits © ABB Group May 26, 2011 | Slide 16
- 17. ConclusionsFuture Work Future work desired by ABB: More robust reverse engineering tools Model transformations from UML to Q-ImPrESS Tools and best practices for data collection © ABB Group May 26, 2011 | Slide 17
- 18. © ABB Group May 26, 2011 | Slide 18
Hinweis der Redaktion
- ABB is not a software company, but ABB creates software-intensive systems in power generation, process automation and robotics. We have applied QI on a process control system from ABB, whose user interface is depicted in this photo. A process control system can be used in power plants, chemical plants, oil refineries, etc. It is a distributed, service-oriented system, which consists of a number of field devices, controllers, servers and client PCs. The system parts we analysed consist of more than 3 million lines of source code.3 MLOC C++, COM, ATL9 subsystems, >100 componentsmanaging industrial process (e.g., power generation, paper production, oil and gas refining, etc.)distributed system, controllers, servers, networks, field devicesoperator workplace for controlling the process: montoring sensor readings, manipulating actuators
- This slide depicts the version history of one ABB process control system. The first version was released in 2004, but today the system is still being maintained and enhanced. Please do not try to read all the text on the slide. It is only meant to illustrate, that the system undergoes a number of evolutionary changes after release. Many of these changes have an impact on the performance, reliability, and maintainability of the system.
- This is a screenshot of the Q-ImPrESS workbench. It supports model the architecture of a system using graphical editors and then allows to simulate the performance and reliablity of the architecture or different alternatives.
- ABB is not a software company, but ABB creates software-intensive systems in power generation, process automation and robotics. We have applied QI on a process control system from ABB, whose user interface is depicted in this photo. A process control system can be used in power plants, chemical plants, oil refineries, etc. It is a distributed, service-oriented system, which consists of a number of field devices, controllers, servers and client PCs. The system parts we analysed consist of more than 3 million lines of source code.3 MLOC C++, COM, ATL9 subsystems, >100 componentsmanaging industrial process (e.g., power generation, paper production, oil and gas refining, etc.)distributed system, controllers, servers, networks, field devicesoperator workplace for controlling the process: montoring sensor readings, manipulating actuators
- units obfuscated for confidentiality reasonssubsystem 8 has highest failure probabilitysubsystem 1 has highest sensitivity to system reliabilitysubsystem 6 is used by many subsystems, but only limited contribution to system reliability