What ever happened to #NoProjects? – post-projects

AllanAdmin from Allan Kelly Associates

“I’m frankly amazed at how far the #NoProjects throwaway Twitter comment travelled. But even today, in the bank where I work, the same problems caused by project-oriented approach to software are manifest as the problems I saw at xxxx xxx years ago.” Joshua Arnold

Once upon a time, 2 or 3 years back, #NoProjects was a hot topic – so hot it was frequently in flames on Twitter. For many of the #NoProjects critics it was little different from #NoEstimates. It sometimes felt that to mention either on Twitter was like pulling the pin and tossing a hand grenade into a room.

I never blocked anyone but I did mentally tune out several of those critics and ignore their messages. However I should say thank you to them, in the early days they did help flesh out the argument. In the later days were a great source of publicity. If we wanted to publicise an event one only had to add #NoProjects to a tweet and stand back.

And now?

There are at least 3 books on the subject: #NoProjects by Evan Laybourn and Shane Hastie, Live happily ever after without Projects by Dimitri Favre and my own Project Myopia, plus the companion Continuous Digital. (You can get Project Myopia for free by signing up to the email version of this blog.)

The hashtag still gets used but far less often, the critics have fallen back and rarely give battle and as I’ve said before #NoProjects won. But, as a recent conversation on the old #NoProject Slack channel asked: why do we still have projects? why does nobody activity say they do #NoProjects?

In part that is because No doesn’t tell you what to do, it tells you what not to do, so what do you do?

In retrospect we didn’t have the language to express what we were trying to say, over time with the idea floating around we found that language: Outcome oriented, Teams over Projects, Products over projects, Product centric, Stable teams and similar expressions all convey the same idea: its not about doing a project, its not even about doing agile, it is about creating sustainable outcomes and business advantage.

The same thinking is embedded in AgendaShift, “The Spotify Model”, SAFe and other frameworks. These are continuity models rather than the stop-go project model. One might call all these ideas and models post-project thinking.

In many ways the hashtag died because we found better, and less confrontational, language to express ourselves.

There was a growing, if implicit, understanding that this is digital not IT, it is about digital business, and that means continuity. The project model of IT is dead.

Which begs the question: why aren’t these approaches more widespread?

The thinking is there, the argument has been made against projects and for alternative models, and you would be hard pressed to find a significant advocate of agile who would argue differently but companies are still, overwhelmingly, project oriented.

When I’m being cynical I’d say, like agile, it is a generational thing. The current generation of leaders – or at least those in positions of management authority – build their success on delivering IT projects. Only as this generation relinquishes leadership will things change.

Optimistically I remember what science fiction author William Gibson once said:

“The future is here, its just unevenly spread around”

For digital start-ups this isn’t an issue: they are born post-project, they create digital products, the business and technology are inseparable. The project model is counter to their DNA.

Some legacy companies have consciously gone post-project and are recognising the benefits: the capitalist model suggests these early movers 9 risk takers – will gain the most. Other legacy companies have adopted parts of the continuous model but cling to the project model too, some will make the full jump, some, most?, will fall back.

Unfortunately Covid, the hang over of bail-outs from the 2007-8 financial crash and failure to break up monopolies (Google, Facebook, Amazon specifically) mean capitalism is not exerting its usual Darwinian force.

Projects will exist for a long time yet, #NoProjects will continue small scale disruption but in the long term the post-project organizations will win out. Hopefully I’ll be alive to see it but I have no illusion, the rest of my career will be spent undoing the damage the project model does.

The post What ever happened to #NoProjects? – post-projects appeared first on Allan Kelly Associates.

Learning useful stuff from the Ecosystems chapter of my book

Derek Jones from The Shape of Code

What useful, practical things might professional software developers learn from the Ecosystems chapter in my evidence-based software engineering book?

This week I checked the ecosystems chapter; what useful things did I learn (combined with everything I learned during all the other weeks spent working on this chapter)?

A casual reader would conclude that software engineering ecosystems involved lots of topics, with little or no theory connecting them. I had great plans for the connecting theories, but lack of detailed data, time and inspiration means the plans remain in my head (e.g., modelling the interaction between the growth of source code written in a particular language and the number of developers actively using that language).

For managers, the usefulness of this chapter is the strategic perspective it provides. How does what they and others are doing relate to everything else, and what patterns of evolution are to be expected?

Software people like to think that everything about software is unique. Software is unique, but the activities around it follow patterns that have been followed by other unique technologies, e.g., the automobile and jet engines. There is useful stuff to be learned from non-software ecosystems, and the chapter discusses some similarities I have learned about.

There is lots more evidence of the finite lifetime of software related items: lifetime of products, Linux distributions, packages, APIs and software careers.

Some readers might be surprised by the amount of discussion about what is now historical hardware. Software needs hardware to execute it, and the characteristics of the hardware of the day can have a significant impact on the characteristics of the software that gets written. I suspect that most of this discussion will not be that useful to most readers, but it provides some context around why things are the way they are today.

Readers with a wide knowledge of software ecosystems will notice that several major ecosystems barely get a mention. Embedded systems is a huge market, as is Microsoft Windows, and very many professional developers use C++. However, to date the focus of most research has been around Linux and Android (because its use of Java, a language often taught in academia), and languages that have a major package repository. So the ecosystems chapter presents a rather blinkered view of software engineering ecosystems.

What did I learn from this chapter?

Software ecosystems are bigger and more complicated that I had originally thought.

Readers might have a completely different learning experience from reading the ecosystems chapter. What useful things did you learn from the ecosystems chapter?

Play and create little retro games at Smolpxl

Andy Balaam from Andy Balaam's Blog

I love simple games: playing them and writing them.

But, it can be overwhelming getting started in the complex ecosystems of modern technology.

So, I am writing the Smolpxl library, which is some JavaScript code that makes it quite simple to write simple, pixellated games. It gives you a fixed-size screen to draw on, and it handles your game loop and frames-per-second, so you can focus on two things: updating your game “model” – the world containing all the things that exist in your game, and drawing onto the screen.

I am also writing some games with this library, and publishing them at smolpxl.artificialworlds.net:

I am hoping this site will gradually gain more and more Free and Open Source games, and start to rival some of the advertising-supported sites for the attention of casual gamers, especially kids.

Smolpxl is done for fun, and for its educational value, so it should be a safer place for kids than a world of advertising, loot boxes and site-wide currencies.

As I write games, I want to show how easy and fun it can be, so I will be streaming myself live doing it on twitch.tv/andybalaam, and putting the recordings up on peertube.mastodon.host/accounts/andybalaam and youtube.com/user/ajbalaam.

I am hoping these videos will serve as tutorials that help other people get into writing simple games.

Would you like to help? If so:

shareon.js.org now has a Share to Mastodon button

Andy Balaam from Andy Balaam's Blog

I was looking for the right way to make a “Share This”-style button for my tiny games site Smolpxl, and I found shareon which worked exactly the way I wanted (load the JavaScript and call a function to display the buttons, with no privacy concerns), and looked really nice.

The only thing that was missing was a Mastodon button.

“Share to Mastodon” is more complicated than something like Share to Twitter, because Mastodon is not one web site, but lots of web sites that all talk to each other.

So, after someone clicks “Share to Mastodon”, you need to ask them which web site (or Mastodon instance) they meant.

I started out by hacking a Mastodon button in after the shareon ones, and prompting the user for their instance. This was a little unfriendly, but it worked:

Click Share, Mastodon, enter instance URL into ugly browser prompt, and end up at Mastodon

But luckily I didn’t stick with that. Because I think shareon is awesome, and because I want more people to use Mastodon, I decided to try adding a Mastodon button to shareon. I wrote the code to work similarly to my original hack, and submitted a Pull Request.

I am really glad I did that, because what followed was a really positive Free and Open Source Software mini-interaction. Nick Karamov responded with lots of improvements and bug fixes to my original change, and as we discussed the problem more, I expressed the desire for a proper page to choose Mastodon instance, that would be more friendly than a simple prompt. I also said that it would be difficult.

In retrospect, maybe suggesting it would be difficult was a clever trick, because the next thing I knew, Nick had implemented just such a page: toot.karamoff.dev!

Because toot.karamoff.dev now existed, the “Share to Mastodon” button became much simpler: we can send our post information to toot.karamoff.dev, and it asks which Mastodon instance you want to use, and passes it on the correct place.

So my new Pull Request was much simpler than the original, and with a few more improvements suggested by Nick, it’s merged and I have a usable Share to Mastodon button without hacking it in.

The cake has a little more icing too, because I was also able to improve toot.karamoff.dev by adding code that downloads the up-to-date list of Mastodon instances from joinmastodon.org and provides them as suggestions, which can be really helpful if you can’t remember the exact name of your instance. Again, Nick’s suggestions on my Pull Request were incredibly helpful and made the code way better than what I originally wrote. Now it works really smoothly:

Click Share, Mastodon, choose instance from a friendly list on toot, and end up at Mastodon

In a small way, this was a fantastic example of how effective and fun working on Free and Open Source Software can be.

Piping Software for Less: Why, What & How (Part 1)

Paul Grenyer from Paul Grenyer

Developing software is hard and all good developers are lazy. This is one of the reasons we have tools which automate practices like continuous integration, static analysis and measuring test coverage. The practices help us to measure quality and find problems with code early. When you measure something you can make it better. Automation makes it easy to perform the practices and means that lazy developers are likely to perform them more often, especially if they’re automatically performed every time the developer checks code in.

This is old news. These practices have been around for more than twenty years. They have become industry standards and not using them is, quite rightly, frowned upon. What is relatively new is the introduction of cloud based services such as BitBucket Pipelines, CircleCI and SonarCloud which allow you to set up these practices in minutes, however with this flexibility and efficiency comes a cost.

Why

While BitBucket Pipelines, CircleCI and SonarCloud have free tiers there are limits.

With BitBucket Pipelines you only get 50 build minutes a month on the free tier. The next step up is $15/month and then you get 2500 build minutes.

On the free CircleCI tier you get 2500 free credits per week, but you can only use public repositories, which means anyone and everyone can see your code. The use of private repositories starts at $15 per month.

With SonarCloud you can analyse as many lines of code as you like, but again you have to have your code in a public repository or pay $10 per month for the first 100,000 lines of code.

If you want continuous integration and a static analysis repository which includes test coverage and you need to keep your source code private, you’re looking at a minimum of $15 per month for these cloud based solutions and that’s if you can manage with only 50 build minutes per month. If you can’t it’s more likely to be $30 per month, that’s $360 per year.

That’s not a lot of money for a large software company or even a well funded startup or SME, though as the number of users goes up so does that price. For a personal project it’s a lot of money. 

Cost isn’t the only drawback, with these approaches you can lose some flexibility as well. 

The alternative is to build your own development pipelines. 

I bet you’re thinking that setting up these tools from scratch is a royal pain in the arse and will take hours; when the cloud solutions can be set up in minutes. Not to mention running and managing your own pipeline on your personal machine and don’t they suck resources when they’re running in the background all the time? And shouldn’t they be set up on isolated machines? What if I told you, you could set all of this up in about an hour and turn it all on and off as necessary with a single command? And if you wanted to, you could run it all on a DigitalOcean Droplet for around $20 per month. 

Interested? Read on.

What

When you know how, setting up a continuous integration server such as Jenkins and a static analysis repository such as SonarQube in a Docker container is relatively straightforward. As is starting and stopping them altogether using Docker Compose. As I said, the key is knowing how; and what I explain in the rest of this article is the product of around twenty development hours, a lot of which was banging my head against a number of individual issues which turned out to have really simple solutions.

Docker

Docker is a way of encapsulating software in a container. Anything from an entire operating system such as Ubuntu to a simple tool such as the scanner for SonarQube. The configuration of the container is detailed in a Dockerfile and Docker uses Dockerfiles to build, start and stop containers. Jenkins and SonarQube all have publically available Docker images, which we’ll use with a few relatively minor modifications, to build a development pipeline.

Docker Compose

Docker Compose is a tool which orchestrates Docker containers. Via a simple YML file it is possible to start and stop multiple Docker containers with a single command. This means that once configured we can start and stop the entire development pipeline so that it is only running when we need it or, via a tool such as Terraform, construct and provision a DigitalOcean droplet (or AWS service, etc.) with a few simple commands and tear it down again just as easily so that it only incurs cost when we’re actually developing. Terraform and DigitalOcean are beyond the scope of this article, but I plan to cover them in the near future. 

See the Docker and Docker Compose websites for instructions on how to install them for your operating system.

How

In order to focus on the development pipeline configuration, Over this and a few other posts I’ll describe how to create an extremely simple Dotnet Core class library with a very basic test and describe in more detail how to configure and run Jenkins and SonarQube Docker containers and setup simple projects in both to demonstrate the pipeline. I’ll also describe how to orchestrate the containers with Docker Compose. 

I’m using Dotnet Core because that’s what I’m working with on a daily basis. The development pipeline can also be used with Java, Node, TypeScript or any other of the supported languages. Dotnet Core is also free to install and use on Windows, Linux and Mac which means that anyone can follow along.

A Simple Dotnet Core Class Library Project

I’ve chosen to use a class library project as an example for two reasons. It means that I can easily use a separate project for the tests, which allows me to describe the development pipeline more iteratively. It also means that I can use it as the groundwork for a future article which introduces the NuGet server Baget to the development pipeline.

Open a command prompt and start off by creating an empty directory and moving into it.

mkdir messagelib
cd messagelib

Then open the directory in your favorite IDE, I like VSCode for this sort of project. Add a Dotnet Core appropriate .gitignore file and then create a solution and a class library project and add it to the solution:

dotnet new sln
dotnet new classLib --name Messagelib
dotnet sln add Messagelib/Messagelib.csproj

Delete MessageLib/class1.cs and create a new class file and class called Message:

using System;

namespace Messagelib
{
    public class Message
    {
        public string Deliver()
        {
            return "Hello, World!";
        }
    }
}

Make sure it builds with:

dotnet build

Commit the solution to a public git repository or you can use the existing one in my bitbucket account here: https://bitbucket.org/findmytea/messagelib

A public repository keeps this example simple and although I won’t cover it here, it’s quite straightforward to add a key to a BitBucket or GitHub private repository and to Jenkins so that it can access them.

Remember that one of the main driving forces for setting up the development pipeline is to allow the use of private repositories without having to incur unnecessary cost.


Read the next parts here:




Sidebar 1


Continuous Integration 

Continuous Integration (CI) is a development practice where developers integrate code into a shared repository frequently, preferably several times a day. Each integration can then be verified by an automated build and automated tests. While automated testing is not strictly part of CI it is typically implied.


Static Analysis

Static (code) analysis is a method of debugging by examining source code before a program is run. It’s done by analyzing a set of code against a set (or multiple sets) of coding rules.


Measuring Code Coverage

Code coverage is a metric that can help you understand how much of your source is tested. It's a very useful metric that can help you assess the quality of your test suite.



Sidebar 2: CircleCI Credits

Credits are used to pay for your team’s usage based on machine type and size, and premium features like Docker layer caching.



Sidebar 3: What is Terraform?

Terraform is a tool for building, changing, and versioning infrastructure safely and efficiently. Terraform can manage existing and popular service providers as well as custom in-house solutions.

Configuration files describe to Terraform the components needed to run a single application or your entire datacenter. Terraform generates an execution plan describing what it will do to reach the desired state, and then executes it to build the described infrastructure. As the configuration changes, Terraform is able to determine what changed and create incremental execution plans which can be applied.

The infrastructure Terraform can manage includes low-level components such as compute instances, storage, and networking, as well as high-level components such as DNS entries, SaaS features, etc.


Piping Software for Less: Jenkins (Part 2)

Paul Grenyer from Paul Grenyer



Run Jenkins in Docker with Docker Compose

Why use Jenkins I hear you ask? Well, for me the answers are simple: familiarity and the availability of an existing tested and officially supported Docker image. I have been using Jenkins for as long as I can remember. 

The official image is here: https://hub.docker.com/r/jenkins/jenkins

After getting Jenkins up and running in the container we’ll look at creating a ‘Pipeline’ with the Docker Pipeline plugin. Jenkins supports lots of different ‘Items’, which used to be called ‘Jobs’, but Docker can be used to encapsulate build and test environments as well. In fact this is what BitBucket Pipelines and CircleCI also do.

To run Jenkins Pipeline we need a Jenkins installation with Docker installed. The easiest way to do this is to use the existing Jenkins Docker image from Docker Hub. Open a new command prompt and create a new directory for the development pipeline configuration and a sub directory called Jenkins with the following Dockerfile in it: 

FROM jenkins/jenkins:lts

USER root
RUN apt-get update
RUN apt-get -y install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \
    software-properties-common

RUN curl -fsSL https://download.docker.com/linux/debian/gpg | apt-key add -

RUN add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/debian \
   $(lsb_release -cs) \
   stable"

RUN apt-get update
RUN apt-get install -y docker-ce docker-ce-cli containerd.io
RUN service docker start

# drop back to the regular jenkins user - good practice
USER jenkins

You can see that our Dockerfile imports the existing Jenkins Docker image and then installs Docker for Linux. The Jenkins image, like most Docker images, is based on a Linux base image.

To get Docker Compose to build and run the image, we need a simple docker-compose.yml file in the root of the development pipeline directory with the details of the Jenkins service:

version: '3'
services:
  jenkins:
    container_name: jenkins
    build: ./jenkins/
    ports:
      - "8080:8080"
      - "5000:5000"
    volumes:
        - ~/.jenkins:/var/jenkins_home
        - /var/run/docker.sock:/var/run/docker.sock

Note the build parameter which references a sub directory where the Jenkins Dockerfile should be located. Also note the volumes. We want the builds to persist even if the container does not, so create a .jenkins directory in your home directory:

mkdir ~/.jenkins

Specifying it as a volume in docker-compse.yml tells the Docker image to write anything which Jenkins writes to /var/jenkins_home in the container to ~/.jenkins on the host - your local machine. If the development pipeline is running on a DigitalOcean droplet, DigitalOcean Volumes can be used to persist the volumes even after the droplet is torn down.

As well as running Jenkins in a Docker container we’ll also be doing our build and running our tests in a Docker container. Docker doesn’t generally like being run in a Docker container itself, so by specifying /var/run/docker.sock as a volume, the Jenkins container and the test container can be run on the same Docker instance.

To run Jenkins, simply bring it up with Docker compose:

docker-compose up

(To stop it again just use ctrl+c)

Make sure the first time you note down the default password. It will appear in the log like this:

Jenkins initial setup is required. An admin user has been created and a password generated.

Please use the following password to proceed to installation:

<password>

This may also be found at: /var/jenkins_home/secrets/initialAdminPasswor

To configure Jenkins for the first time open a browser and navigate to:

http://localhost:8080

Then:

  1. Paste in the default password and click continue.
  2. Install the recommended plugins. This will take a few minutes. There is another plugin we need too which can be installed afterwards.
  3. Create the first admin user and click Save & Continue.
  4. Confirm the Jenkins url and click Save & Finish.
  5. Click Start Jenkins to start Jenkins.

You now have Jenkins up and running locally in a Docker container! 

  1. To use Docker pipelines in Jenkins we need to install the plugin. To do this:
  2. Select Manage Jenkins from the left hand menu, followed by Manage Plugins.
  3. Select the ‘Available’ tab, search for ‘Docker Pipeline’ and select it,
  4. Click ‘Download now and install after restart’. 
  5. On the next page put a tick in the ‘restart after download’ check box and wait for the installation and for Jenkins to restart. Then log in again.

Next we need to create the Docker Pipeline for the Messagelib solution. 

  1. Select ‘New Item’ from the left hand menu, enter ‘Messagelib’ as the name, select ‘Pipeline’ and click ok.
  2. Scroll to the ‘Pipeline’ section and select ‘Pipeline script from SCM’ from the ‘Definition’ dropdown. This is because we’re going to define our pipeline in a file in the Messagelib solution. 
  3. From the ‘SCM’ dropdown, select ‘Git’ and enter the repository URL of the Messagelib solution. 
  4. Then click Save.


Jenkins is now configured to run the Messagelib pipeline, but we need to tell it what to do by adding a text file called Jenkinsfile to the root of the Messagelib solution.

/* groovylint-disable CompileStatic, GStringExpressionWithinString, LineLength */

pipeline
{
    agent
    {
        docker { image 'pjgrenyer/dotnet-build-sonarscanner:latest' }
    }
    stages
    {
        stage('Build & Test')
        {
            steps
            {
                sh 'dotnet clean'
                sh 'dotnet restore'
                sh 'dotnet build'
            }
        }
    }
}

This very simple Groovy script tells the Jenkins pipeline to get the latest ‘dotnet-build-sonarscanner’ Docker image and then use it to clean, restore and build the dotnet project. ‘dotnet-build-sonarscanner’ is a Docker image I built and pushed to Docker Hub using the following Dockerfile:

FROM mcr.microsoft.com/dotnet/core/sdk:latest AS build-env
WORKDIR /
RUN apt update
RUN apt install -y default-jre
ARG dotnet_cli_home_arg=/tmp
ENV DOTNET_CLI_HOME=$dotnet_cli_home_arg
ENV DOTNET_CLI_TELEMETRY_OPTOUT=1
ENV PATH="${DOTNET_CLI_HOME}/.dotnet/tools:${PATH}"
ENV HOME=${DOTNET_CLI_HOME}
RUN dotnet tool install --global dotnet-sonarscanner
RUN chmod 777 -R ${dotnet_cli_home_arg}

This creates and configures a development environment for Dotnet Core and Sonar Scanner, which requires Java. 

There is a way to use the Dockerfile directly, rather than getting it from Docker Hub, described here: https://www.jenkins.io/doc/book/pipeline/docker/

Once the Jenkins file is added to the project and committed, set the build off by clicking ‘Build now’ from the left hand menu of the MessageLib item. The first run will take a little while as the Docker image is pulled (or built). Future runs won’t have to do that and will be quicker. You should find that once the image is downloaded, the project is built quickly and Jenkins shows success.


Read the next parts here:


Part 2: Piping Software for Less: Jenkins (Part 2)
Part 3: Piping Software for Less: SonarQube (Part 2)

Learning useful stuff from the Projects chapter of my book

Derek Jones from The Shape of Code

What useful, practical things might professional software developers learn from the Projects chapter in my evidence-based software engineering book?

This week I checked the projects chapter; what useful things did I learn (combined with everything I learned during all the other weeks spent working on this chapter)?

There turned out to be around three to four times more data publicly available than I had first thought. This is good, but there is a trap for the unweary. For many topics there is one data set, and that one data set may not be representative. What is needed is a selection of data from various sources, all relating to a given topic.

Some data is better than no data, provided small data sets are treated with caution.

Estimation is a popular research topic: how long will a project take and how much will it cost.

After reading all the papers I learned that existing estimation models are even more unreliable than I had thought, and what is more, there are plenty of published benchmarks showing how unreliable the models really are (these papers never seem to get cited).

Models that include lines of code in the estimation process (i.e., the majority of models) need a good estimate of the likely number of lines in the final software system. One issue that nobody had considered was the impact of developer variability on the number of lines written to implement the same functionality, which turns out to be large. Oops.

Machine learning has infested effort estimation research. What the machine learning models actually do is estimate adjustment, i.e., they do not create their own estimate but adjust one passed in as input to the model. Most estimation data sets are tiny, and only contain a few different variables; unless the estimate is included in the training phase, the generated model produces laughable results. Oops.

The good news is that there appear to be lots of recurring patterns in the project data. This is good news because recurring patterns are something to be explained by a theory of software project development (apparent randomness is bad news, from the perspective of coming up with a model of what is going on). I think we are still a long way from having workable theories, but seeing patterns is a good sign that one or more theories will be possible.

I think that the main takeaway from this chapter is that software often has a short lifetime. People in industry probably have a vague feeling that this is true, from experience with short-lived projects. It is not cost effective to approach commercial software development from the perspective that the code will live a long time; some code does live a long time, but most dies young. I see the implications of this reality being a major source of contention with those in academia who have spent too long babbling away in front of teenagers (teaching the creation of idealized software that lives on forever), and little or no time building software systems.

A lot of software is written by teams of people, however, there is not a lot of data available on teams (software or otherwise). Given the difficulty of hiring developers, companies have to make do with what they have, so a theory of software teams might not be that useful in practice.

Readers might have a completely different learning experience from reading the projects chapter. What useful things did you learn from the projects chapter?

Further Still On A Very Cellular Process – student

student from thus spake a.k.

My fellow students and I have lately been spending our spare time experimenting with cellular automata, which are simple mathematical models of single celled creatures such as amoebas, governing their survival and reproduction from one generation to the next according to the population of their neighbourhoods. In particular, we have been considering an infinite line of boxes, some of which contain living cells, together with rules that specify whether or not a box will be populated in the next generation according to its, its left hand neighbour's and its right hand neighbour's contents in the current generation.
We have found that for many such automata we can figure the contents of the boxes in any generation that evolved from a single cell directly, in a few cases from the oddness or evenness of elements in the rows of Pascal's triangle and the related trinomial triangle, and in several others from the digits in terms of sequences of binary fractions.
We have since turned our attention to the evolution of generations from multiple cells rather then one; specifically, from an initial generation in which each box has an even chance of containing a cell or not.

Visual Lint 7.0.9.324 has been released

Products, the Universe and Everything from Products, the Universe and Everything

This is a recommended maintenance update for Visual Lint 7.0. The following changes are included:

  • When a custom report folder is defined in the Options Dialog "Reports" page, generated reports will now be written into subfolders identifying the solution/workspace, analysis tool and analysed solution/workspace configuration rather than just the solution/workspace name. This allows analysis reports for the same project but using different analysis tools or configurations to co-exist without overwriting each other.

  • Fixed a bug in the persistence of the "Generate reports in..." report options in the Options Dialog "Reports" page.

  • Updated the PC-lint Plus message database to reflect changes in PC-lint Plus 1.3.5.

Download Visual Lint 7.0.9.324