Month: November 2017

The practice of continuous integration (CI) is well established within traditional C development environments and is at the core of agile workflows.  Model-Based Design processes are able to extend the base level of CI testing through the use of simulation to validate functional behavior.  While this is possible in traditional environments it requires the development of specific test harnesses which the MBD environment provides for “free”

Continuous Integration: Background

There are 5 best practices for CI systems

1. All source objects must exist in version control: Source objects are used to create derived objects such as C code or test results.  The derived objects do not need to be kept in the repository.
2. All operations on the objects must be automated: Operations include code generation, running test, collecting code metrics.
3. Any commitment to the repository should trigger a build and test operation:  The “level” of test can be set for each check-in.  If the impact of the check-in is localized then a subset of the tests should be run.
4. Results from build and test are easy to interpret: Every build and test operation should generate a report.  Results in that report should be easy to interpret and, in the case of errors, easy to trace back to the root cause of the error (e.g. which file resulted in the failure.)
5. Minimize build and test execution time: To the extent possible the execution time of the build and test operations should be minimized.

The continuous integration workflow

The continuous integration workflow has two primary paths, an event, and temporal based triggers.  With the temporally-based trigger, the testing environment is run at predetermined intervals, generally nightly and weekly test runs.  With the event-based triggers, the testing environment is “triggered” by the check-in of a model or related data objects.

Testing levels

To improve the speed of test execution the concept of test “levels” is introduced.  At each level, the pass/fail criteria are assessed and if passed the next level for that model can be run; failure results halts further testing.

Level	Info	Runs
Level 1	Smoke tests to ensure that the model updates and passes basic modeling guideline checks	Check-in, Nightly, Weekly
Level 2	Functional testing against requirements	Checkin, Weekly
Level 3	Coverage and functional testing	Weekly

Final considerations

There are multiple continuous integration servers such as Jenkins and TeamCity.   Further, there are multiple version control programs that can be combined with the CI servers (such as Git or SVN).

The key consideration is the set up of testing infrastructure “patterns” that enable simple integration with the CI system down the road.  These testing patterns will be covered in a future post.

First a specification, with this post I am addressing the rollout of Model-Based Design in large corporations, e.g. companies with at least 5 divisions using the Model-Based Design methodologies.   Information on rollouts for smaller companies can be found in the post on “group roll out” in the Model-Based Design roadmap.

The discussion of deploying Model-Based Design across a company requires reviewing the working groups and roles as well as how to maintain common tools and methods across the company.

Roles within the company

There are three roles to address:

1. Model-Based Design Working Group (MBD-WG)
2. Model-Based Design Tool and Support Group (MBD-TSG)
3. Model-Based Design Director (MBD-D)

Model-Based Design Working Group

The MBD-WG is composed of technical and process experts from each division.  In the initial company rollout, their role is to present their divisions best practices for MBD and, as a group, determine what the common best practices will be.  Once the initial process is delivered their role is to

1. Suggest improvements to the process
2. Identify issues with common process for their division
3. Act as point of contact for their division on MBD

Model-Based Design Tools & Support Group

The MBD-TSG role is to

1. Provide the common infrastructure for Model-Based Design
2. Provide training on the use of the Model-Based Design infrastructure
3. Analyse changes to the MBD tool suite for desirable features
   (e.g. review new releases of tools for features that will provide additional or missing functionality)

Model-Based Design Director

The director role is unique to larger corporations.  Their role is to both arbitrate between groups and to set the overall vision of how Model-Based Design will be implemented within the company.  To that end, there are several requirements for the person filling the role, both technical and managerial aspects.

1.) Understanding of traditional software development processes and Model-Based Design processes

The most obvious is an understanding of the fundamentals of traditional software development and Model-Based Design.  In the role of Model-Based Design Director, they will be responsible for articulating the reasons for the transition to MBD.  This leads to the second requirement.

2.) Experience arbitrating between competing objectives

It is common for different divisions to have developed different approaches to Model-Based Design.  The role of MBD-D includes arbitration between the members of the Working Group to help develop the common infrastructure plan.  In order to do this, the director needs to become familiar with the process and requirements of each division.  This leads to the third requirement.

3.) Dedicated time to the Model-Based Design establishment effort

A common failure point in the establishment of a company-wide Model-Based Design process is having the director’s time split between multiple projects.  During the first three to five years of the establishment effort having the director focused on the establishment of the MBD process is critical.  This includes time to visit each division, meeting with the working group, attending conferences on Model-Based Design.

4.) Ability to articulate the Model-Based Design vision

The final role is to provide information to groups both inside and outside of the active divisions.  Changing processes requires consistent commitment and having a person at the “top” who can articulate a vision greatly enhances the chance that the adoption will succeed.

Final thoughts

The rollout across a large organization presents a number of unique challenges that smaller companies do not face.  These challenges derive from the unique methodologies that each division may have developed during their implementation process.  To minimize these issues the sooner a Model-Based Design Director and MBD-WG can be established the more effective the rollout will be.

 

 

 

In past posts, I have written about writing understandable models (Simulink and Stateflow.)  With this post, I want to address measures of clarity (or it’s inverse complexity.)  Note: in this post, I will be focusing specifically on Simulink metrics as contrasted with C based metrics.

Measurements in the C world

So what should be measured and how do you evaluate the measurements?  In traditional C based development there multiple metrics such as…

1. Lines of code (LOC):  A simple measure of overall project “size”.
   Note: A sub-metric is lines of code per function (LOC/Func)
2. Coding standard compliance: A set of guidelines for how code should be formatted and structured (e.g. MISRA)
3. Cyclomatic complexity: A measure of the number of logical paths through the program
4. Depth of inheritance: A C++ measure of how deep the class definition extends to the “root” class.  Can be applied to function call depth as well.
5. Reuse: The degree to which code is reused in the project.
   Note: a better measure is the degree of reuse across projects but this is more difficult to capture with automated tools.
6. Coupling/Cohesion: Measures of the direct dependencies of modules on other modules.  Lose coupling supports modular programming
7. Much more… : A list of some additional code metrics can be found here:  Software Metrics : Wikipedia

Model-Based Design metrics

Within the Model-Based Design world, there are both direct and analogous metrics of the C-based versions.

1. Total block count (TBC): The total block count maps onto the LOC metric.  Likewise, a Blocks per Atomic Subsystem can be compared to the LOC/Function metric.
2. Guideline compliance: Modeling guidelines, such as the MAAB, map on to C based guidelines.
3. Model complexity: Maps onto cyclomatic complexity.  It should be noted that the model complexity and cyclomatic complexity of the generated code will be close but not exact.
4. Subsystem/reference depth: A measure of how many layers of hierarchy exist in the model
5. Reuse: The use of libraries and referenced models that can directly
6. Coupling: Simulink models do not have an analogous metric for coupling.  By their nature, they are atomic units without coupling.
7. Much more….

Evaluating measurements

There is no such thing as a perfect model or perfect bit of C code.  In the end, all of the metrics above are measured against pass/fail thresholds.  For example, common thresholds for the Model Metrics include

1. Blocks per atomic subsystem < 50
2. Guideline compliance > 90%
   Note: with some guidelines that must be passed regardless of percentage
3. …

 

Measuring the model

With Models, as with text-based development environments, there are a variety of tools for collecting metrics.  Within the Simulink environment, it is possible to write stand-alone scripts to automate this process or use the Model Metrics tool to automatically collect this information.

In a previous post I wrote about fidelity in plant models.  For this post I want to focus, briefly, on three fundamental plant modeling tools that can be used to create simple and fast plant models.

1. Table-lookup functions and adaptive tables
2. Regression functions
3. Transfer functions

Lookup tables

Lookup tables form the backbone of many plant models.  From thermodynamic property lookups to engine torque and power curves they allow users to quickly access multi-input / single output data.

The efficiency and memory usage of tables can be be dramatically improved when the conditions of their use is well understood.  Common methods include

1. Index search from last index:  In cases of slow moving data it is faster to start the indexing into the table by starting from the last time steps value.
2. Reuse of index across multiple tables:  In many instances the same X or Y axis will be used across multiple tables.  The calculation cost for finding the index can be decreased through a pre-lookup function.
3. Chose the correct interpolation function:  The efficiency of tables is dependent on the interpolation function.
4. Pick and condition your data:  Frequently data from real world measurements is not evenly spaced.  By preconditioning the data (e.g. smoothing out data and providing even spacing for axis…)

Regression functions

Regression functions allow for modeling of non-linear multi-variable functions.  Two cautions regression functions.  First is in ensuring that data is valid throughout the input variable range and that the accuracy is sufficient at all points in the range.

The second caution is to validate that the regression equation does not become computational burdensome.

Transfer functions

The final “simple” plant model that I want to cover is the use of transfer functions.  Transfer functions can be used to model a system when the mathematical equations describing the system are know and involve state (e.g. derivative) information.  (Note for multi-input, multi-output functions State Space equations can be used)

Transfer functions have the advantage over table look ups and regression functions that the represent the actual underlying physical system from a theoretically correct perspective.   In some instances the function may need to be “tuned” to take into accounts imperfections in real world systems.

Final thoughts

In the end there are many different methods for creating plant models.  Frequently the “correct” approach is to use a combination of “simple” and “advanced” modeling methods.  Take the time to think about what you require from your plant and the correct combinations can be selected.

 

An Interface Control Document (ICD) can be defined as

A document that describes the interface(s) to a system or subsystem. It may describe the inputs and outputs of a single system or the interface between two systems or subsystems.

Within the traditional textual based development process, an ICD was either a text-based document or, a UML type diagram or a combination of the two.  Within the MBD development process, it is not entirely clear if additional supporting documentation is required or if the Model can serve as the ICD.

With most topics that I write about I have reached a firm conclusion on what is the accepted best practice.  In this area, I still have open questions.  In this post, I lay out the pros and cons for using models as ICDs.

Why models are sufficient:

The simplest argument as to why models are sufficient is that the models can be used in place of UML diagrams provided the interface has the sufficent markup.  For example in the image below the Simulink Interface View provides the data types and rates of all the inputs and outputs to the system.

When the model is part of a model hierarchy than the calling structure can be derived from the model.  (Simulink Model Dependency View)

Why models are lacking:

While the two views above are good, they lack information that is commonly found in ICD documents; the function interface (e.g. the C or C++ calling methods) and the data interface.  The models contain and use this information however they do not, natively, display the information.   Note: this is a limitation of UML diagrams as also.

The next issue with models as an ICD document is a question of “push and pull.”  By having the model as a development artifact and the ICD document you need to implement a change request process.

What can be done?

Use of automatic report generation can augment the information provided natively by the model.  Doing this could, in fact, generate a “standard” text-based ICD with the advantage being that the model would stay the single source of truth.

As with most issues in the development where there is not a native tool it is the implementation of a process that helps to bridge the gap.  All ready for text-based ICDs people have change request processes in place.  The question with an MBD approach is who implements the change at the model, and at the system level?

As always, please feel free to share your thoughts in the comment section.