Imagine two different types of scoring scenarios

• Hours – number of man-hours it takes to complete a complex software creation schedule
• ScalesUp – feature of software that makes it scale to near infinite number of users

Both of these are actually Saturation based scoring models, but only theoretically. In practice, there is no hope of ever reducing the number of hours to zero, so really hours is always a MoreIsBetter model – the more efficient the process – in this case the least number of hours, the better.

• 2863 hours: good
• 2714 hours: better

But ScalesUp is like most other scoring aspects of creating software. It is a goal which is actually quite attainable in a very finite sense. Once you have built software which can and does deploy to near infinite scale, you are pretty much done with that. It works, and more, in this case is not better. You can’t improve much on perfection. This is the Saturation model, and that model can be easily represented as a 1-100 score, where 100 is the highest score.

• score of 0 – 0% complete
• score of 70 – 70% complete
• score of 99 – 99% complete

We can conclude then that each of these fits the opposite scoring model

• Hours: MoreIsBetter model
• ScalesUp: Saturation model

Scoring the Saturation Model

This is something that is not obvious until you start the process of scoring for Saturation in a specific score. It takes a different kind of thinking to score against a Saturation model when there are a number of different options that each contribute to a score.

Arbitrary Subtotals

When scoring for saturation, you know that the score can never exceed 100, but you also know that many different activities may be required to meet that goal. This might spread across several options, or in the case of Smoslt: SideEffects as Options

Let’s take an oversimplified example of scalesUp score:

• Scalable Software: software written to operate across as many machines as required
• Cloud Provider: relationship with a vendor to provide as many machines as required
• DevOps: deployment written to recognize and react to increased need

You can’t get to 100 without each of these aspects being complete, so really you have to be able to score each separately. The percentage of score allocated to each is arbitrary, that they should total to 100% is not arbitrary – it is a given.

• Scalable Software option: 25%
• Cloud Provider option: 17%
• DevOps option: 68%

Only now can you begin the process of scoring. For each of the above 3 options, you have the tasks of separately scoring completion against their respective target.

Saturation Based Scoring Within a SideEffect

To explore what it means to score a specific option that affects a saturation score, let’s look at the simpler option of Cloud Provider above. Note first, that this option may affect several different score types, including all of these below, most of which are saturation based scoring models.

• Saturation
  • ScalesUp
  • ScalesDown
  • Durability
  • ManagerSpeak
  • FeedbackSpeed
• MoreIsBetter
  • Hours
  • POLR
  • LongTerm

This breakdown may or may not be representative of what really belongs in Saturation based scoring, but let’s take one that is pretty clear – ScalesUp. This is because an app either scales up properly or it doesn’t. It can easily be measured and tested, and it is pretty obvious when it fails.

So now we know that within ScalesUp, repeating from above, each of these options contributes an arbitrarily apportioned part of this score:

• Scalable Software option: 25%
• Cloud Provider option: 17%
• DevOps option: 68%

If we look at only the CloudProvider option, or side effect, we then need to do these things

• Set the apportionment of the score at 17%
• As the necessary work is complete, increment score against that 17%
• When this work is complete, you have fully incremented this score by no more or no less than 17

 Another Challenge of the Saturation Modeling: 100%

Saturation is based on an index of 0 to 100%. This sounds perfectly logical, and it can even be logical in implementations. Take ScalesUp for example. If you are using Cassandra for your persistence store and have the appropriate use case, at least for the persistence piece you are at 100%. Cassandra scales linearly right out of the box. Can’t get any closer to 100% than that. So if you have Cassandra in your mix, make your ScalesUp score 100%. Right?

Not so fast there, buddy. Let’s take another look.

Your project has a lot more pieces than just a persistence score. Each of these pieces can wreck a ScalesUp score. If your persistence score scales perfectly, but you have a web tier and a messaging tier too, and they don’t scale up well, then you’re not at 100% yet. So now you have to modify your scoring such that web tier an messaging tier and persistence store together add up to 100%. Is that a third each? Or does web tier get 50% and persistence store 40% and messaging tier get 10%. Good question.

guidelines

• avoid anything that requires re-architecting later
• do anything that can set aside straightforward additions later

here is my baseline

• all projects are MJWA OSGi ready jars, but not running in OSGi container
• all my projects do not use carefully architected checked exceptions
  • but rather just stupid RuntimeExceptions to fix later
• do not do real testing, but do use junit to just get the methods running
• do use neil ford’s composed method but only enough to keep my job easier, not religiously

here are my options

• foo

my baseline vs other baselines

• versus Marcos baseline
• versus Matt baseline
• versus ….

Command Line Application for SMOSLT

Glossary

• PL: ProjectLibre
• [compliant]: ProjectLibre file which conforms to exact specifications expected for SMOSLT.stacker – see separate document

Features

• import a [compliant] PL file and run SMOSLT.assume against same
• something here about two files, one for baseline another for latest something
• maybe something here about narratives or options or generating

• all baseline tasks
• no automated options (generated by SMOSLT)
• resources individually named, with exact group that belongs to
• all tasks assigned with either
  • specified individual by exact name
  • specified group(s) by exact name, and count
  • comma delimited

Somewhere need to note that

cartoon of evaluating options process

from situation come up with type of … to look up prototype/template

copy template into my…

modify template to reflect situation

add resources to match task titles

add/modify predecessor relationships

add/modify resources to reflect situation

add/modify options modifiers to reflect situation

rerun to extend out into actual schedule

primary goal is allows you to not have to pick two

• keep things fluid
• not limiting visibility into options
• allow you to have less than complete information

“This or this or this option, but not more than one from this group”

What Or-Grouping is NOT:

SMOSLT, as it relates to software options, offers a way to evaluate where to commit your limited resources. Should I organize the build around a Continuous Integration server? Or commit those same resources, instead, to deploying my services to smaller linux container modules?

• Continuous Integration
• Docker Container Service Deployments

These concerns each require a commitment of resources, and I only have enough resources to do one, but neither do they overlap. I could do both, if I had enough resources.

What Or-Grouping IS:

If I decide to commit resources to Continuous Integration, I’m still not done with the comparison of options. That’s where or-grouping comes in. Consider these options for Continuous Integration servers:

• Jenkins
• Hudson
• Thoughtworks Go server
• Bamboo

I need to pick whichever one of these options makes the most sense for my organization.

I would never choose more than one of these, it’s an either/or choice. Pick one.

How Or-Grouping Fails in SMOSLT.options module:

The magic of SMOSLT.options is that, unlike it’s human operator, it can compare every combination of options given to it.

Yet this same feature, without or-grouping, has an unintended side effect. For example, it might cause Jenkins and Bamboo to be selected for comparison at the same time! Wrong! The human would know that you either use Jenkins or Bamboo to achieve Continuous Integration, but you would never use both in combination! SMOSLT.options has no way of knowing that, without or-grouping.

How To Use Or-Grouping:

Or-grouping is implemented via naming conventions. Consider again, the same list of candidates for SMOSLT.options to compare:

• Jenkins
• Hudson
• ThoughtworksGo
• Bamboo

To implement an Or-Group, we rename this same list as follows:

• Ci1-Jenkins
• Ci2-Hudson
• Ci3-ThoughtworksGo
• Ci4-Bamboo

SMOSLT.options module now knows to never evaluate any combination of two or more of these options at the same time. For example, using red bold to indicate selected options, SMOSLT.options would not evaluate the following combination:

• Ci1-Jenkins

• Ci2-Hudson

• Ci3-Thoughtworks Go server

• Ci4-Bamboo

Scoring and Inheritance with Or-Groups

Each of these CI server options is more alike, than they are different. Differences between Continuous Integration servers exist, but the big difference is not between them, but between using a CI server and not using a CI server. Again, the list, only this time the name of the java file that does the scoring.

• Ci1-Jenkins.java
• Ci2-Hudson.java
• Ci3-ThoughtworksGo.java
• Ci4-Bamboo.java

Scoring each of these means writing each of the above java class, and then copying and pasting the common scoring code into each, and changing whatever is unique after copying and pasting.

We all know the problems of maintaining copy-pasted code. Not good.

So instead, we refactor the above group to add a common super-class. Now the or-group class structure looks like this.

• Ci0-ContinuousIntegration.java

• Ci1-Jenkins.java – extends Ci0-ContinuousIntegration
• Ci2-Hudson.java – extends Ci0-ContinuousIntegration
• Ci3-ThoughtworksGo.java – extends Ci0-ContinuousIntegration
• Ci4-Bamboo.java – extends Ci0-ContinuousIntegration

Now we can put the common scoring code in  Ci0-ContinuousIntegration.java

and the other classes will only contain the scoring code that pertains to that unique server.

Spreadsheet Reporting

The existence of an or-grouping in a SMOSLT.options run alerts the SMOSLT.analytics module that you care about comparing various Continuous Integration options.

So the SMOSLT.analytics module prepares a separate tab in the spreadsheet document, just to compare those options. It names this tab, appropriately, “Continous Integration”

Or-Group Score Summarization?

As mentioned above, you have two primary issues when looking at Continuous Integration for your project.

1. Should I even do Continuous Integration at all, or devote resources to something else?
2. If I do, which of the many attractive servers should I choose to implement?

SMOSLT.analytics will prepare a spreadsheet with potentially many sheets to help you with this and other options. As stated above, it will even prepare a tab within that spreadsheet to help you with number 2 – choosing between or-group options

The analytics piece does NOT, however, help you aggregate or summarize Continuous Integration servers for number 1. If you give it 4 or-group options, it will show each individually, making your spreadsheet potentially harder to re read when deciding whether to commit resources to Continuous Integration or some other option. For that reason, you may wish to make a series of separate runs. Try this sequence, for example.

1. Pick Ci1-Jenkins alone, in your first runs, to compare what happens when you commit resources to Continuous Integration versus committing resources to other options such as Docker Deployments.
2. Once you’ve decided that Continuous Integration is probably going to be included in your project plan, then make some more runs with each of the rest of the CI 0r-group included. That will let you compare various CI servers and make a final decision