The only thing you can do wrong, and the opposite of agile

Allan Kelly from Allan Kelly

The only thing you can do wrong in agile is to work the same way you did 3 months ago.

Corollary: The opposite of agile is static.

“The only thing you can do wrong” is born out of two things: my belief that agile is all about learning and putting learning into action.

Second, the proliferation of “agile tests” – maturity models, things like the Nokia Scrum test and the mental models in our own minds which condemn teams . Failing these tests means you label the team something like “Agile in name only”, “ScumBut” or “ScrummerFall”. I’ve seen my own share of these teams but honestly, I don’t care. Motivation to change a bigger issue, as long a the team are trying to improve its just a question of time – you might be “AINO” but if you keep trying you will become “Kick-ass agile.” (Plus, as an agile consultant these teams are potential clients, send them to me!)

If you are learning and attempting to act on that learning you will make some false moves, you will need to undo some changes but over time you will move forward.

You need to learn in three domains: Solution domain – the tools and technology you use to craft solutions and systems; Application or problem domain – understanding the problem/opportunity you are trying to solve/exploit, understanding what customers’ need and what the market will pay for. (I tend to think of this as the demand side); and the Process domain – the way you work, your processes and practices, the most obvious place were “agile” fits in.

The boundaries between these domains are fluid you need a learning culture in all three.

More recently I realised that “the only thing you can do wrong” has a corollary:

        The opposite of agile is static, not learning and not changing

20 years ago agile advocates invented Waterfall to be the enemy – sure the cascade model, à la Royce, was industry standard but nobody, NOBODY, called it Waterfall – believe me, I’m old enough to remember first hand. Agile was described by reference to what agile was not, and that not was named “waterfall.”

But that is wrong.

The secret is in the words: Agile implies movement and action.

Not moving is called Static, so the opposite of agile is static.

Once you accept agile means movement and change the next question is: how fast?

Rather than agile tests and “agile maturity models” we should be to measuring the speed of change and the speed of improvement: how fast are the team learning and acting on that learning? how successful are they at that?

When I floated this suggestion on LinkedIn a few days ago a few people pushed back. One argument was that I was advocating “change for change’s sake”. I’m not. You are free to not change, you are free to not change if you wish, all I’m saying is don’t call that agile, ‘cos its not, that is static.

Not changing is static, and static is not agile.

Now maybe static is the right thing for you. If your team can repeat their way of working, if they are predictable, and if the team and stakeholders are content then then change! Embrace static.

I’m doubtful such a static position is anything other than a short term Stable Intermediate Form. Static implies a stable environment, one in which at all forces are in equilibrium: nobody is asking for faster delivery, nobody is changing their ask, nobody is complaining about technical debt, overwork, predictability and so on. If people are not complaining then celebrate, you are living the dream! Just don’t call it agile.

“Finished” software is static because nobody changes it. Software ages because it stays unchanged while the world around it changes.

A second argument was that constant change was not good for people. Now I’m not arguing for constant big change, or constant top-down change. In my mind agile change is largely incremental – sure sometimes you need a big change but most of the changes are small.

I also challenge the assumption that change is top-down and that change is done to people. In my experience when those doing the work are enrolled in the change process, when they can see the potential benefits then it is a different matter. In my experience “change resistance” is more likely “resistance to being changed.”

Finally, “last 3 months”. That time frame comes from my intuition, it is a period long enough to see if change has happened without demanding that the team are never repeating themselves. I imagine the team moving from one Stable Intermediate Form to another Stable Intermediate Form over those 3 months. Feel free to suggest another time frame but if you think 3 months is not long enough to see change then how long? 4 months? 6 months? 12 months?


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User Stories by Example: certifcate added to the free courses

Allan Kelly from Allan Kelly

A couple of months ago I made my online User Stories by Example tutorial series free. One client suggested that the series would benefit from a certificate at the end. Good idea, I’m always open to suggestions.

So, I’ve added a new tutorial to the series: exam and certificate – rather than just give a certificate of attendance to anyone who plays the videos I’ve set a little test. There is a bank of questions which are randomly selected and should cover the five areas of the tutorials. Score over 60% on the test and you get a certificate which lists the key topics covered.

I’ve set a small fee for this, once you have paid you have 21 days to do the exam and you can sit the exam as many times as you like.

As always, if you have any suggestions or other feedback please let me know. And if you have any ideas for new questions send them over, I’d love to increase the question pool.


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Improving my vimrc live on stream

Andy Balaam from Andy Balaam's Blog

I was becoming increasingly uncomfortable with how crufty my neovim config was getting, and especially how I didn’t understand parts of it, so I decided to wipe it clean and rebuild it from scratch.

I did it live on stream, to make it feel like a worthwhile activity:

Headline features of the new vimrc:

  • A new theme, using the base16 theme framework
  • A file browser (NERDTree)
  • A minimalist status line with vim-airline
  • Search with ripgrep
  • Rust language support with Coc

Note: after the stream I managed to resolve the remaining issues with highlight colours not showing by triggering re-applying them after the theme has been applied:

augroup tabs_in_make
    autocmd!
    autocmd ColorScheme * highlight MatchParen cterm=none ctermbg=none ctermfg=green
augroup END

You can find my current neovim config at gitlab.com/andybalaam/configs/-/tree/main/.config/nvim.

Evidence-based Software Engineering: now in paperback form

Derek Jones from The Shape of Code

I made my Evidence-based Software Engineering book available as a pdf file. While making a printed version available looked possible, I was uncertain that the result would be of acceptable quality; the extensive use of color and an A4 page size restricted the number of available printers who could handle it. Email exchanges with several publishers suggested that the number of likely print edition copies sold would be small (based on experience with other books, under 100). The pdf was made available under a creative commons license.

Around half-million copies of the pdf have been downloaded (some partially).

A few weeks ago, I spotted a print version of this book on Amazon (USA). I have no idea who made this available. Is the quality any good? I was told that it was, so I bought a copy.

The printed version looks great, with vibrant colors, and is reasonably priced. It sits well in the hand, while reading. The links obviously don’t work for the paper version, but I’m well practised at using multiple fingers to record different book locations.

I have one report that the Kindle version doesn’t load on a Kindle or the web app.

If you love printed books, I heartily recommend the paperback version of Evidence-based Software Engineering; it even has a 5-star review on Amazon 😉

Programming language similarity based on their traits

Derek Jones from The Shape of Code

A programming language is sometimes described as being similar to another, more wide known, language.

How might language similarity be measured?

Biologists ask a very similar question, and research goes back several hundred years; phenetics (also known as taximetrics) attempts to classify organisms based on overall similarity of observable traits.

One answer to this question is based on distance matrices.

The process starts by flagging the presence/absence of each observed trait. Taking language keywords (or reserved words) as an example, we have (for a subset of C, Fortran, and OCaml):

            if   then  function   for   do   dimension  object
C            1     1       0       1     1       0        0
Fortran      1     0       1       0     1       1        0
OCaml        1     1       1       1     1       0        1

The distance between these languages is calculated by treating this keyword presence/absence information as an n-dimensional space, with each language occupying a point in this space. The following shows the Euclidean distance between pairs of languages (using the full dataset; code+data):

                C        Fortran      OCaml
C               0        7.615773     8.717798
Fortran      7.615773    0            8.831761
OCaml        8.717798    8.831761     0

Algorithms are available to map these distance pairs into tree form; for biological organisms this is known as a phylogenetic tree. The plot below shows such a tree derived from the keywords supported by 21 languages (numbers explained below, code+data):

Tree showing relative similarity of languages based on their keywords.

How confident should we be that this distance-based technique produced a robust result? For instance, would a small change to the set of keywords used by a particular language cause it to appear in a different branch of the tree?

The impact of small changes on the generated tree can be estimated using a bootstrap technique. The particular small-change algorithm used to estimate confidence levels for phylogenetic trees is not applicable for language keywords; genetic sequences contain multiple instances of four DNA bases, and can be sampled with replacement, while language keywords are a set of distinct items (i.e., cannot be sampled with replacement).

The bootstrap technique I used was: for each of the 21 languages in the data, was: add keywords to one language (the number added was 5% of the number of its existing keywords, randomly chosen from the set of all language keywords), calculate the distance matrix and build the corresponding tree, repeat 100 times. The 2,100 generated trees were then compared against the original tree, counting how many times each branch remained the same.

The numbers in the above plot show the percentage of generated trees where the same branching decision was made using the perturbed keyword data. The branching decisions all look very solid.

Can this keyword approach to language comparison be applied to all languages?

I think that most languages have some form of keywords. A few languages don’t use keywords (or reserved words), and there are some edge cases. Lisp doesn’t have any reserved words (they are functions), nor technically does Pl/1 in that the names of ‘word tokens’ can be defined as variables, and CHILL implementors have to choose between using Cobol or PL/1 syntax (giving CHILL two possible distinct sets of keywords).

To what extent are a language’s keywords representative of the language, compared to other languages?

One way to try and answer this question is to apply the distance/tree approach using other language traits; do the resulting trees have the same form as the keyword tree? The plot below shows the tree derived from the characters used to represent binary operators (code+data):

Tree showing relative similarity of languages based on their binary operator character representation.

A few of the branching decisions look as-if they are likely to change, if there are changes to the keywords used by some languages, e.g., OCaml and Haskell.

Binary operators don’t just have a character representation, they can also have a precedence and associativity (neither are needed in languages whose expressions are written using prefix or postfix notation).

The plot below shows the tree derived from combining binary operator and the corresponding precedence information (the distance pairs for the two characteristics, for each language, were added together, with precedence given a weight of 20%; see code for details).

Tree showing relative similarity of languages based on their binary operator character representation and corresponding precedence.

No bootstrap percentages appear because I could not come up with a simple technique for handling a combination of traits.

Are binary operators more representative of a language than its keywords? Would a combined keyword/binary operator tree would be more representative, or would more traits need to be included?

Does reducing language comparison to a single number produce something useful?

Languages contain a complex collection of interrelated components, and it might be more useful to compare their similarity by discrete components, e.g., expressions, literals, types (and implicit conversions).

What is the purpose of comparing languages?

If it is for promotional purposes, then a measurement based approach is probably out of place.

If the comparison has a source code orientation, weighting items by source code occurrence might produce a more applicable tree.

Sometimes one language is used as a reference, against which others are compared, e.g., C-like. How ‘C-like’ are other languages? Taking keywords as our reference-point, comparing languages based on just the keywords they have in common with C, the plot below is the resulting tree:

Tree showing similarity of languages based on the keywords they share with C.

I had expected less branching, i.e., more languages having the same distance from C.

New languages can be supported by adding a language file containing the appropriate trait information. There is a Github repo, prog-lang-traits, send me a pull request to add your language file.

It’s also possible to add support for more language traits.

On Pitfall – student

student from thus spake a.k.

Recall that in the Baron's latest wager, Sir R-----'s goal was to traverse a three by three checkerboard in steps determined by casts of a four sided die, each at a cost of two coins. Moving from left to right upon the first rank and advancing to the second upon its third file, thereafter from right to left and advancing upon the first file and finally from left to right again, he should have prevailed for a prize of twenty five coins had he landed upon the top right place. Frustrating his progress, however, were the rules that landing upon a black square dropped him back down to the first rank and that overshooting the last file upon the last rank required that he should move in reverse by as many places with which he had done so.

Devin Townsend at the Royal Albert Hall (again)

Paul Grenyer from Paul Grenyer

Leprous

There’s an obvious pull for me towards Leprous due to the association with Ihsahn and prog, but rock bands generally do little for me these days. I listened to a little of Aphelion before the gig, but it didn’t grip me.

They’re an odd live band and some of the time the cello player looked a bit out of place when he was without his cello. The sharing of the keyboards among various band members, often in the same song, was also weird. The singer was wearing a waistcoat and doing some very odd dancing and his voice can grate. For a prog band the lack of any guitar or keyboard lead breaks was also weird.

However, I quite enjoyed Leprous!


Devin Townsend

We’d only seen Devin Townsend a few months ago (in the summer at Bloodstock), but my wife loves him so we went again. We should have gone the night before as he played loads of songs we knew, in contrast to the night we went where he played nothing we knew! Most of it, I am reliably informed, was from the Ocean Machine and Infinity albums.

Devin still plays brilliantly and it was great to see him again with the session musicians he’d teamed up with for his Bloodstock performance. He creates a fantastic wall of sound and engages with the crowd like few others. I’m sure we’ll go and see him again, after all we’ve not heard Hyperdrive live yet!

Agile OKRs extra – yet another book

Allan Kelly from Allan Kelly

I blogged last week that I had begun work on a new book – How I Write Books which is now a work in progress at LeanPub – signup and be the first to know when the draft is published.

Well a funny thing happened while I was setting up my tool chain to write that book: I found another book! Well, perhaps half a book is a better description.

Succeeding with OKRs in Agile Extra is a companion to last year’s best seller, Succeeding with OKRs in Agile. But it isn’t a complete book in its own right, it isn’t really a sequel, it is a companion. It contains a mix of material. Material which didn’t really fit in the first book, material with was’t needed, ideas which didn’t develop far enough and some unfinished chapters.

As such it is like my Xanpan Appendix, unused material which is still interesting and might appear elsewhere in time.

I really want to work on How I write books so I don’t have any immediate plans to progress extra. If you enjoyed Succeeding with OKRs in Agile, if you would like to know more, or if you would like to just see how a writer’s mind works check out Succeeding with OKRs in Agile Extra.

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Ecology as a model for the software world

Derek Jones from The Shape of Code

Changing two words in the Wikipedia description of Ecology gives “… the study of the relationships between software systems, including humans, and their physical environment”; where physical environment might be taken to include the hardware on which software runs and the hardware whose behavior it controls.

What do ecologists study? Wikipedia lists the following main areas; everything after the first sentence, in each bullet point, is my wording:

  • Life processes, antifragility, interactions, and adaptations.

    Software system life processes include its initial creation, devops, end-user training, and the sales and marketing process.

    While antifragility is much talked about, it is something of a niche research topic. Those involved in the implementations of safety-critical systems seem to be the only people willing to invest the money needed to attempt to build antifragile software. Is N-version programming the poster child for antifragile system software?

    Interaction with a widely used software system will have an influence on the path taken by cultures within associated microdomains. Users adapt their behavior to the affordance offered by a software system.

    A successful software system (and even unsuccessful ones) will exist in multiple forms, i.e., there will be a product line. Software variability and product lines is an active research area.

  • The movement of materials and energy through living communities.

    Is money the primary unit of energy in software ecosystems? Developer time is needed to create software, which may be paid for or donated for free. Supporting a software system, or rather supporting the needs of the users of the software is often motivated by a salary, although a few do provide limited free support.

    What is the energy that users of software provide? Money sits at the root; user attention sells product.

  • The successional development of ecosystems (“… succession is the process of change in the species structure of an ecological community over time.”)

    Before the Internet, monthly computing magazines used to run features on the changing landscape of the computer world. These days, we have blogs/podcasts telling us about the latest product release/update. The Ecosystems chapter of my software engineering book has sections on evolution and lifespan, but the material is sparse.

    Over the longer term, this issue is the subject studied by historians of computing.

    Moore’s law is probably the most famous computing example of succession.

  • Cooperation, competition, and predation within and between species.

    These issues are primarily discussed by those interested in the business side of software. Developers like to brag about how their language/editor/operating system/etc is better than the rest, but there is no substance to the discussion.

    Governments have an interest in encouraging effective competition, and have enacted various antitrust laws.

  • The abundance, biomass, and distribution of organisms in the context of the environment.

    These are the issues where marketing departments invest in trying to shift the distribution in their company’s favour, and venture capitalists spend their time trying to spot an opportunity (and there is the clickbait of language popularity articles).

    The abundance of tools/products, in an ecosystem, does not appear to deter people creating new variants (suggesting that perhaps ambition or dreams are the unit of energy for software ecosystems).

  • Patterns of biodiversity and its effect on ecosystem processes.

    Various kinds of diversity are important for biological systems, e.g., the mutual dependencies between different species in a food chain, and genetic diversity as a resource that provides a mechanism for species to adapt to changes in their environment.

    It’s currently fashionable to be in favour of diversity. Diversity is so popular in ecology that a 2003 review listed 24 metrics for calculating it. I’m sure there are more now.

    Diversity is not necessarily desired in software systems, e.g., the runtime behavior of source code should not depend on the compiler used (there are invariably edge cases where it does), and users want different editor command to be consistently similar.

    Open source has helped to reduce diversity for some applications (by reducing the sales volume of a myriad of commercial offerings). However, the availability of source code significantly reduces the cost/time needed to create close variants. The 5,000+ different cryptocurrencies suggest that the associated software is diverse, but the rapid evolution of this ecosystem has driven developers to base their code on the source used to implement earlier currencies.

    Governments encourage competitive commercial ecosystems because competition discourages companies charging high prices for their products, just because they can. Being competitive requires having products that differ from other vendors in a desirable way, which generates diversity.

How I write books – A book about books

Allan Kelly from Allan Kelly

In the last 15 years I’ve written and published 3 books with publishers, published 5 books myself, plus edited one conference proceedings and pushed out three “mini books” (one with 3 editions) which I never publicised.

In addition I’ve contributed forwards and chapters to at least six books and had two books translated.

Then there are countless magazine and journal articles but they stretch back further, closer to 25 years – and this blog from 2005.

Not bad for a kid who was thrown out of school after asking a teacher how to spell “at” – age of eight, a diagnosed dyslexic who had to learn to read three times – I can’t read my own handwriting its so bad.

As a result I’ve learned a lot about writing and publishing. In the last few years I’ve spoken to many people who want to know how to write and publish their own book. A couple of years ago Steve Smith suggested I write a book about writing books. I’ve been avoiding that until this month.

Now I’ve started: How I write books, https://leanpub.com/howIwrite – sign-up to be the first to known when the MVP is published. And if there is anything you would like me to write about in the book please let me know.

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