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

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  • 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

ICSE 2011: Q-ImPrESS - An Industrial Case Study on Quality Impact Prediction ICSE 2011: Q-ImPrESS - An Industrial Case Study on Quality Impact Prediction Presentation Transcript

  • © 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
  • Industrial automation: Process Control Systems
    © ABB Group
    May 26, 2011 | Slide 2
  • 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
  • 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
  • Q-ImPrESS MethodOverview
  • Q-ImPrESS Workbench
  • Case Study
    © ABB Group
    May 26, 2011 | Slide 7
  • Manual ModelingSteps executed to create a Q-ImPrESS model
    © ABB Group
    May 26, 2011 | Slide 8
    ~2.5 person months
  • Manual ModelingQ-ImPrESS model of the ABB process control system
    © ABB Group
    May 26, 2011 | Slide 9
  • Performance PredictionSteps executed to determined resource demands
    © ABB Group
    May 26, 2011 | Slide 10
    ~1 person month
  • Performance PredictionSample predictions for different design alternatives
    © ABB Group
    May 26, 2011 | Slide 11
  • Performance PredictionResults: Measurements vs. Simulation Results
    © ABB Group
    May 26, 2011 | Slide 12
  • Reliability PredictionSteps executed to determine failure probabilities
    © ABB Group
    May 26, 2011 | Slide 13
    ~1 person month
  • 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
  • Reliability PredictionResults
    © ABB Group
    May 26, 2011 | Slide 15
     More research and tool development needed
  • 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
  • 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
  • © ABB Group
    May 26, 2011 | Slide 18