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Building Views with IBM Engineering Lifecycle Optimization – Engineering Insights (ENI) Part One – Introduction

In this series of articles, we’ll be exploring how to build views in IBM Engineering Lifecycle Optimization – Engineering Insights (ENI) that allow you to visualize and analyze your engineering data in a powerful, dynamic way. We’ll see just how quickly and easily these views can be built, starting with some very basic concepts before progressing to more advanced topics. In this first article, we’ll explore what ENI (formerly called Rational Engineering Lifecycle Manager or RELM) is and what it can do for you.

What is Engineering Insights?

The Engineering Environment

In any large organization, the engineering environment can be highly fragmented. Many tools are in use, each with its own user interface, database or file system repository and workflow. And yet for a robust engineering environment, the data in these tools must be connected.

The Importance of Traceability

Traceability between artifacts is critical in complex engineering projects, especially those which have to consider compliance to standards such as DO178B/C, ISO 26262, Automotive SPICE and so on. Traceability is one of the cornerstones of change impact analysis (and if you can be sure of one thing – it is that change is inevitable in a project). This kind of traceability has historically been done using point-to-point, bespoke tool integrations, which are brittle and prone to breaking when one of the tools changes versions. Alternatively, the traceability may have been maintained separately in other documents such as spreadsheets – a cumbersome, time-and-labor-intensive and potentially error-prone process. Yet another way traceability has been maintained is by taking an ‘import all the data into a single tool’ approach (which simply doesn’t scale).

OSLC

The advent of Open Services for Lifecycle Collaboration (OSLC) solved this problem by allowing the data to stay where it belongs;  in the tools that best know how to author and manage it, whilst allowing that data to be linked to related data in other tools using standard HTTP-based protocols. So requirements, for example, stay in the requirements management tool but can be connected to design elements in any design tool that supports the standard.

Engineering Insights

So what has all of this got to do with ENI?

Whilst OSLC allows the creation of these links, it does not address how to view and analyze them, at least not in any holistic way. When a requirement changes, how quickly can you assess the impact of that change? The designs, the test cases, the physical parts that all might be modified or replaced? These kinds of analyses can take days, even weeks to perform and are costly and error-prone. Engineering Insights allows searching, visualization and dynamic analysis of the linked lifecycle data, allowing tasks like impact analysis to be performed in minutes.

Misconceptions

Whilst ENI has other capabilities such as global searching and auto-generation of impact analysis graphs, this series of articles will focus on how to build views that allow dynamic analysis and visualization of our engineering data. Obviously, impact-analysis/traceability style views are particularly useful (and if you’ve seen ENI before it’s likely that you saw an impact analysis view) but don’t fall into the trap of thinking that ENI is only useful for impact analysis. In fact, I’ve heard several misconceptions around ENI, such as:

  • “ENI is only useful for Impact Analysis”
    • ENI has a far wider scope than this – in a moment we’ll see a few examples
  • “ENI is only useful if you have the entire suite of Jazz tools”
    • Not true at all, ENI is equally useful for analyzing a single data source. For example, it can provide a view of the status of work items across all EWM projects or provide gap analysis on requirements that goes way beyond the ERM traceability tree.
  • “ENI is only useful for Engineering”
    • Despite the name, Engineering Insights is equally applicable to finance, IT and so on. It analyses data, and as long as that data is visible to ENI (more on that later)it doesn’t matter at all what the nature of that data is.

Sample Views

To highlight the previous points, here are some sample views that have been built with ENI:

Traceability View

Traceability View

Yes, this one is an impact analysis view – starting from a single artifact, in this case, a Story work item in IBM Engineering Workflow Management (EWM). This is actually the first view we’ll build in this series of articles, and we’ll be using the out of the box data set so you can deploy a sandbox on Jazz.net and try this for yourself. Note that the test results are color-coded by their pass/fail status. Note also that the view is live – you can invoke rich hover on any of these artifacts, and even use ENI to navigate to any of these artifacts in the correct tool, and in the correct configuration context (ENI is GC-aware)

Traceability View – The Big Picture

Traceability View - the Big Picture

An alternative to showing impact analysis for a single artifact – show it for all of them. We can go from the first traceability view to this one and back again with a couple of clicks of the mouse. No other single tool can give you this kind of view.

Performing Calculations

Budget / Cost Analysis

Budegt Cost Analysis

This view is analyzing a single data source – IBM Engineering Workflow Management (EWM). By traversing the internal links between different work items, it performs calculations and highlights where a parent task is over or under budget, based on its child tasks, the resources allocated to those tasks, the time estimation of the tasks and the cost of the allocated resources. The view is also dynamic – clicking on a parent task will display another view that drills down into the detail:

Budget Detailed View

Requirements Quality Analysis

IBM Engineering Requirements Management can leverage the power of Watson to analyze requirements and score them for quality. The above view(s) present an average of those quality scores, by module and by project, helping to identify project risk or identify those projects that need extra training in how to write better requirements.

ENI for Compliance

Compliance - Requirements Readiness

 

This ‘requirements readiness’ view we built for ASPICE compliance. Only when all of the requirements in a module have a linked test case with a passed result (and thus turn green) does the module turn green. This view is also dynamic, clicking a module on the left populates the details of the requirements on the right.

Traceability Beyond the Jazz Platform

Beyond the Jazz Platform

This view shows artifacts that are not maintained inside the Jazz platform. At the top of the view is a device type coming from the Watson Internet of Things Platform. At the bottom, we can see artifacts from a PLM tool which is not even a product from IBM.

And Now for the Science Bit … The Lifecycle Query Engine

So how does ENI work? One of the core technologies of the Jazz platform is the Lifecycle Query Engine (LQE). LQE builds an index of all of the linked lifecycle data, and it keeps that index up to date by periodically polling any connected tool for updates. Any tool that conforms to the OSLC Tracked Resource Set (TRS) specification can expose its data for LQE to consume, so this capability is not limited solely to the tools on the ELM platform but can also include (for example) PLM data.

LQE then makes this indexed data available to other tools such as Jazz Reporting Services (JRS) Report Builder for self-serve dynamic reporting, and IBM Engineering Lifecycle Optimization – Publishing (formerly called Rational Publishing Engine) for document generation. It also has a REST interface so you can interrogate it yourself.

LQE

The other key tool that consumes this data is, of course, Engineering Insights. Note that the data is protected by access permissions so if your user doesn’t have access to it in the native tool, then you won’t see that data in ENI either.

I hope that’s given you an overview of Engineering Insights and in particular the kinds of views that we can create. In the next article, we’ll get our feet wet and start actually building them!

Andy Lapping
Technical Enablement Specialist
Watson IoT & Engineering Lifecycle Management