The difference is when you're asked for a quote, you're asked how much you will be charging, with the expectations that you'll be willing to eat into your own margins to give a lower quote. That's why it's a negotiation, where you negotiate how much extra effort, time and headcount you're willing to give, how much tech dept you're willing to take, etc., for the privilege of getting their business.

If you see it for what it really is, you'll see that it works pretty well actually. The business gets more out of you for less to them. It was never about having an accurate timeline or helping with planning or prioritizing, and always about negotiating a better contract with the dev team.

Now keep in mind that the "business" in this case is a person who need to report that through their amazing prowess of administration and management, they personally managed to get X feature out during their last review cycle at Y cost with impact Z. This person will not need to deal with developer satisfaction, retention and performance. They will not need to deal with the impact the lower margins they pushed for had on the next feature delivery, or the continued maintainance of the systems. And if the dev team had to lower the quality too much in order to meet the quote they put out, that will be 100% their fault, the "business" will know not to use them for their next contract, or they'll expect the dev team to take on fixing all the issues at their own expense once more.

According to the article, proper research remains a struggle due to outdated datasets from before modern agile methodologies, and that the modern datasets from industry are hard if not impossible to gather.

If industry is truly interested in improving software development and estimation, their data should be anonymized and made available to researchers for analysis.

My own experience has been this: people make estimates, client has expectations based on some variant of those, and something later happens but so much change is introduced during the actual software development, that there seems to be no sane way to compare what happened with original estimates. (new features, changed features, lots of new information, market shifts/product vision shifts, quality assumptions change, etc. etc.)

But at that point nobody cares! People go on with further projects, and no data is ever collected.

Nobody learns.

When f*ckups happen, i.e. a gross over- or under-estimate, this is often not shared with the broader organization (ashamed/afraid sales people/PMs/devs say nothing/hide it/sugarcoat it). Client walks away.

Sometimes project is severly underestimated but usually not just because of the software part. Again, no decoupling and estimation of contributing factors is done.

It's insane.

Software is invention and construction. The construction part is pretty easy to estimate. The invention part is ... very very hard. I'd like to say it's impossible. I'd like to see the software industry use a different word than estimate.

Now I know "most papers are pointless" is a common complaint in science, specially in my area of focus- machine learning. But I can't shake the feeling that the situation is particularly worse in software engineering related academic research.

[1] I saw Mozilla attempt it, but not sure if it's currently in use.

There are just to many unknowns you cannot foresee. Software development is complex.

The problem in software though is that such a repeatable process would be immediately automated away by writing a function, library, framework or any such tools that programmers use on a daily basis without much thinking. Unlike in building construction, to which programming discipline is often wrongly likened to, where construction companies simply cannot "write a function" to deploy cookie cutter houses or bridges one after another.

Therefore software engineering is never a repeatable process, unless crappy tools are used, which don't allow for sufficient abstraction of repeatable parts.

Tasks in software disciplines therefore don't follow a normal distribution. They follow exponential distribution most often. Most issues go unnoticed. Majority are just so tiny and ofthen considered business as usual. Every time you get stuck and have to look up a solution in docs or stackoverflow technically is an issue, but never gets reported in an issue tracker for its triviality. There are however issues which are orders of magnitude larger than what management expects when they occassionally sampling issue trackers. Some issues lead to critical design flaws which could need a full blown rewrite for example, or ever lasting and expensive hackery in case the executive decision is to work around the critical design flaw. These issues can take years to accomplish or take massive amount of pain endurance.

Trying to estimate a process with such exponential distribution and making sense of averages or other statistics of such distribution is borderline insanity.

Why not just go to physics department and ask when the next Theory of Relativity will be invented and how much budget and story points those guys need.

In every single case when you've done that much work, I seem to wind up with a reasonable estimate.

If we've done the job before and have data on it, also reasonable.

Double or triple anything else.

So who are we kidding?

Another way to look at it: take a small one-person project and assign it to three different developers. You may get wildly different results. How could you have predicted those differences in advance? Let alone apply that type of prediction across a large team.

About a dozen years ago I gave a presentation to the Silicon Valley Software Process Improvement Network (does it still exist?) My presentation: "Unsolved Problems of Software Maintenance." You think predicting greenfield development is difficult? Try predicting maintenance work, where figuring out what to do can be more than half the work.

It's easy to detect this:

Gently begin a discussion of how much uncertainty is tolerable, do they want to know the number we are 50% likely to hit? 80%?

If you get emotional pushback to discussing uncertainty, they are looking for a promise.

I found that on average, things take twice as long as expected.

So, I was like, now I know how to estimate any project. I figure out what seems reasonable based on known factors...and double it.

A way to look at this is the "unknown unknowns" of anything empirically average to a 100% overshoot.

But this doesn't fly with the project managers I work with, because they can only see that as illegitimately padding an estimate.

Discussing something that hasn't been rehearsed before? You can really only discuss how long you are willing to work on it. Not how long it will take to get done.

Fun examples. How long would it take you to clean out your fridge? How long would it take you to learn to play a song on piano?

I've had to fight to actually hold post mortems, and every time I've done this, the manager ends up asking, "hey, can I share this?"

So clearly, there's value, at least when we've done them.

I'm amazed at how few places even perform a complete feedback loop. It's just, "when can you get this done?" and, "is it done yet?".

Know how much a Honda Accord costs?

About 25 Grand.

Know how much a Mercedes S450 costs?

About three times as much.

They are both great cars, that will be rewarding to own.

The Mercedes doesn't have 3 times more parts, but it probably took four times longer to make, and they paid the folks that make it, a lot more than the Honda. It's actually, probably better "bang for the buck," although it won't seem like it, on the surface.

The reason is that all those little things that go into high quality take lots of time.

I can write a pretty complete app that does some cool stuff in a day or two. I do it often, when I write test harnesses for my libraries and whatnot.

However, if you want that app to be ship quality, and highly usable, you're looking at over a month.

The thing is, many folks would consider my test harness lash-ups to be their "shipping" product, and will use that as a basis for estimation.

Don't do an estimate, build a prototype.

Don't do an estimate... seriously... build a prototype.

And then throw it out.

And THEN do an estimate. It will probably be pretty accurate.

Not always easy to do this in a tech company but as an eng director I was able to get it done and it changed a seriously broken process based on multi-week scoping and estimation futility.

In my experience, when an estimate is spot on the world goes on as if nothing happened. When it's incorrect, all hell breaks loose and it's every man for himself. And at the end of the day, all of the blame ends up on the person who guessed wrong. I'm glad I don't have to deal with that anymore.

https://vimeo.com/9270320

Bit long, but well-presented and worth a listen for any practicing software developer (or person who manages developers).

Yes, I get on a high level, one needs to be able to say "yes we can make this before [date]", when using big deadlines. How often do people really have to finish something before a critical deadline or decide to drop it right then and there? I'd wager most software devs do not, let alone biweekly deadlines. Isn't agile methodology supposed to help us fight against artificially tight deadlines (customer collaboration)? Isn't the SaaS model combined with "deploy any time" designed to be profitable in accordance to features implemented? Then, why push estimates so strongly in whatever flavor of the month Scrum version BigCorp wishes to use today? What do they even add at that point? If they are really so important, why do we always feel the need to introduce human error when we can extrapolate former experiences with computer models?

Maybe it is just me being cynical. The entire need to hold so fiercely onto an estimate reeks of micromanagement and desire to push responsibility entire onto the lower ranks.

Everyone wants estimates. For decent reasons. But until we've done a similar thing before, they're utter fabrication. We can take a semi-educated guess, but we know they're still guesses... so for things we don't have concrete answers for, we give small/medium/large/extreme markers for how uncertain we are.

It's fine to take on a big unknown or two. You might even get them both done in a half. But they better be worth it (or you have to decide when to cut your losses), because completing those two could consume all of your resources... and if that happens and you didn't commit everyone to it up-front, you won't get it done this half. Making that tradeoff more explicit managed to get us signed up for fewer low-impact-but-highly-uncertain projects that would inevitably balloon out of control but never be cut.

Known knowns: familiar tasks that can be reliably estimated from past experience. You can improve these estimates by better knowledge sharing (internal wiki, adding comments).

Unknown knowns: tasks that can be increasingly better estimated the more time you spend on estimating (for example creating Draft PRs with pseudo code).

Known unknowns: tasks that haven’t been done before, so estimations need a decent buffer (30-50%) to account for research and potential blockers. You can improve these estimates by benchmarking your previous efforts combined with the team's skill levels (aka sprint story point velocity).

Unknown unknowns: the unforeseen blockers that come out of nowhere, can't really be estimated, and can really disrupt a project's schedule. Improving these estimates is really hard. The key to improving these is by improving team/company communication and building agile feedback loops so you can identify these issues early and reprioritize as needed.

- the application is thoroughly specced. You might need WEEKS for this. - all variables are taken care of. Stack is known, and you’ve experience with all parts involved. If you don’t, get familiar with the parts first. Again, might take weeks. - there is no implicit functionality. It is either explicit or not included. - there are clear boundaries and rules to prevent feature creep. - you cannot estimate an estimate - all designs and UX are final

Now the problem is, this estimate is really expensive, because it’s actual work. It takes about 10-25% of the total project time to estimate the project.

> The NASA dataset contains 93 rows (that is not a typo, there is no power-of-ten missing), COCOMO 63 rows, Desharnais 81 rows, and ISBSG is licensed by the International Software Benchmarking Standards Group (academics can apply for a limited time use for research purposes, i.e., not pay the $3,000 annual subscription). The China dataset contains 499 rows, and is sometimes used (there is no mention of a supercomputer being required for this amount of data ;-).

> Why are researchers involved in software effort estimation feeding tiny datasets from the 1980s-1990s into machine learning algorithms?

> Grant money. Research projects are more likely to be funded if they use a trendy technique, and for the last decade machine learning has been the trendiest technique in software engineering research. What data is available to learn from? Those estimation datasets that were flogged to death in the 1990s using non-machine learning techniques, e.g., regression.

Is this telling me that most theories about "sw estimation best practices" are cargo cults o-O ?

I'd wager that data is just as poor overall, but the sheer volume of data available might be able to help get some sort of consistent conclusions from them.

Requirements gathering, general communication, testing, production rollout, coordination, research, final approval - it all adds up. For a 2 week project you might only actually spend a day of ass-in-seat coding. And that ass-in-seat coding is what they tend to estimate.

Estimation is really hard, especially if you're dealing with new challenges, new people, and new expectations. Throwing moving targets (unplanned changes) into the mix, as you noticed, makes estimation even more difficult.

The thing is, it very rarely is the case that a wrong estimate will sink a project. Things get delivered late all the time. No one dies. Most stakeholders aren't like two year olds throwing a tantrum because an unforeseen problem delays a deliverable by a few days (admittedly some are close).

The actual definition of failure also needs to be considered. Is it considered a failure because a project manager ended a colored gantt bar at a particular date and the actual time was longer? Or is it a failure because the customer cancelled the contract because a deliverable was not available on the agreed-upon date? Or is it something far more nebulous, like a slow death march towards a misguided goal that burns everyone out and compels them to sip artisanal coffee and daydream about leaving instead of grinding onwards and hitting targets?

Ultimately, people "don't care" because accurate estimates are not the most critical problem on their mind. Most of the time an accurate answer to "When will it [the deliverable] be done?" has about as much gravitas as guessing the number of jellybeans in a jar at a kid's birthday party.

Doing a retrospective and analyzing what went wrong when a project delivery slipped expectations is certainly reasonable but it so easily slips into a blame storm and/or people making commitments that aren't realistic. Getting straight answers from folks for this kind of analysis requires a level of psychological safety that just isn't present in most workplaces.

For everyone (n00b to senior) I would always recommend designing & breaking things down into no smaller 1/2 day blocks, where nothing went longer than 3 days (for inexperienced), but longer for more experienced. Senior devs should be able to be able to be trusted (to be accurate) for week-long blocks.

Use a Gannt chart (ms project or similar) and monitor the inexperienced, gauging their real-life times with their estimates, adjusting accordingly. Give semi regular feedback at the accuracy so that they (& you, the manager) are working with.

In one of my companies, the most inaccurate we were for any project was that we were late 2 days over 2 years. Build on trust, build on meetings and other time sinks. Include testing, sign offs, etc.

So, absolute n00bs need close mentoring so that the (senior) mentor can begin to gauge the growth rate... Which will be steep... But different for everyone.

For example say you have a CRUD application with a page for editing and searching complex objects. Rather than building the entire search page with all of its filters, and then building the edit page with all of its fields, you build a simplified version of the search page with only a few filters and a simplified version of the edit page with fields that correspond to those filters. You also make sure the functionality integrates with the backend to form a cohesive, vertical feature that can go through QA, and possibly the client. Once that is working you expand the list of fields and filters.

This approach tends to surface design issues much faster, and is more adaptable to change requests from the client.

Add an estimate that you multiply by three in front of each, ie. if the dev thinks 1 hour then they should put 3. From my experiments: no multiplying factor turned to nightly and weekend work, multiplying by 2 turned to long (10 hours) work days, multiplying by 3 turned into comfortable work hours and quality work.

I've had success delivering quality code and respecting deadlines since 2012 or so with this system, but YMMV

Only problem: the customer might not like what he gets, even if it works exactly as planned.

Then you need to train the IT professionals to build the dynamic nature into the conversation and process. You dont quote a number, you give a range. You communicate confidence levels and uncertainty clearly and continuously. You update assumptions as new information comes available. You _push back_ when the uncertainty is frowned on.

It can be done successfully on a micro scale sure, by working on it within an organization and/or team. But its not scalable and its not a 5-step plan. Its hard.

When people ask me how can they learn, I tell them to find old project schedules and other project documents. Looking at past projects makes the learning more objective, and it is not happening under time pressure.

For many software projects, you simply cannot make meaningful granular estimations for parts of them. It doesn't matter how many story-point poker sessions you hold. Some software work cannot be reduced to a positive integer. In our business scopes creep, bugs plague us, and myriad issues make precise estimations pointless. It's engineering Numberwang.

I get that folks want to tame a chaotic world. But sometimes you can't. And the software engineering field in particular feels wildly neurotic about pretending otherwise. Sometimes I imagine applying our project management tools to other technical fields and laugh at how insane we must look.

It gets a little complicated partly because of the industry's penchant for reinventing its tools on a rolling basis. In the trades, technology remains largely unchanged over decades, and only truly useful new advances are integrated into workflows (for example, laser level vs plumb bob or spirit level).

In software, new technological approaches are adopted on a continuous basis, and so a lot of effort is spent on learning how to solve old problems with new tools.

1. The construction part, as you said, can be estimated.

2. I'd just call the other thing "allocated time" instead of "estimated time".

Any time someone asks me how long it will take me to fix a bug that I haven't really looked at yet, or to plan some new feature or something like that, and they badly need a number, I ask them how much time I should allocate to that. I can't promise to have something like that done by that time, but it gives us both an idea about how to treat that problem.

For example, we could allocate two hours to fix a bug, with the understanding that if that turns out to not be enough then we'll need to talk about workarounds. Or we can allocate two days to plan a new feature, and the best solution we can think of in that time shall be the one we use.

For me it often something like this:

I don't have a clue how the hell to do what you asking for. But maybe implementing .. might help. Can't guarantee but it might. I think I can confirm or disprove that spending .. on the prototype subject to following limitations.. If then we find out it actually works for you, we'll go from there but approximately gonna take .. extra to rework the prototype into production-quality stuff. If it won't, I'll think about something else to try.

e.g. say you think it will take a day, so 2 days, 4 days, add another day, likely estimate is 5 days.

My pet theory is that when we estimate, we typically think of how long it will take to figure out a working solution to the problem, but forget about how long it takes to debug it, add tests, rework for changed requirements and unexpected nuances, and then roll it out and do any training, etc.

I make the most optimistic prediction and then * PI.

The part you didn't mention is that for most businesses it's better to be over than under in the estimation. I also explain this thought process to the various stake holders. We can certainly try to tighten up an estimate, but that runs a higher risk of being under which is usually a worse outcome (promised launch dates are missed, marketing is missed/happening, customers are told, etc...).

If it assumes remaining work is equivalent in time to done work (notion of velocity), it's (in my view) very optimistic. People learn the nature of the beast as time progresses. Collective learning happens slowly. A lot of important work is usually only discovered and planned in the second half of the actual (not original) timeline. A lot of important stakeholders are naturally only introduced into projects close to its finalization, which creates a flurry of new activity and discoveries. This makes projects late.

But where that real halftime is - most people I worked with rarely know. I was no better, I also rarely knew.

The only way to manage this I found was the basic rule: build, demo, decide what to do next. Don't get attached to original backlog and grand plans for the future. It won't work that way. Just build and make it work, launch ASAP, get decisions on a ~weekly basis done. Ignore long term planning. It's not helpful.

Sadly, few business folks want that of course :)

The theory of story points (which originate outside of Scrum and are not part of Scrum proper) is that task-specific time estimates in creative intellectual work are extraordinarily unreliable and expending more effort on them doesn't improve them, but broad-banded complexity class evaluation mixed with empirical observation of the teams velocity produces time estimates that are (while still extremely fuzzy) both better and much lower effort, once you have the basic tracking in place, than task-specific direct estimates.

The “dogma” you report seems like something that might be a derivative of that that has lost track of rationale and purpose, reducing it to a cargo cult practice.

But that obviously won't work from the start, and it won't be accurate... Just better than nothing and (hopefully) better than what a programmer will estimate in their head.

And IME, it's a lot less stressful on the programmer to estimate points rather than time.

How to turn your organisation into a cargo cult 101.

Depending on why you are buying a car, the Accord is very likely much better bang for the buck than the S-class Mercedes. And depending on the situation, the prototype is often better value than the shippable product.

You don't want to daily drive a 30 year old performance oriented Mercedes.

An Accord however, most generations can hit 300k miles easily.

The Mercedes is more expensive because they put more investement into refinement and luxury. Make the doors feel right. Ensure the torque curve is flat and starts at a low RPM, usually using turbo chargers that will fail by 200k miles. Lots of carefully placed sound deadening.

This isn't higher quality, it's extra features.

I doubt it. Luxury goods don't proportionately more cost much more to produce than commodity goods.

Instead they often derive their value from perceptions and exclusivity. That is people perceive the object to be more value for their own reasons, or they are made more expensive as a proof point of exclusivity / conspicuous consumption

https://en.wikipedia.org/wiki/Conspicuous_consumption

[1] https://moznainaczej.com.pl/Download/Teal%20Doctrine/A.Blikl... (13.10 Quality and value)

A car's primary function is to move its passengers from point A to point B, safely and timely. Both cars do this very well, but the Mercedes will probably be costlier over their respective lifetime (fuel, parts, service).

The only way for it to be a better bang for buck would be to turn its secondary functions (comfort, prestige, signalling) into a primary tool. For instance, if a broker or salesperson finds that owning the car gives them the added confidence to project the image they want during negotiations and thus contributes to their overall success, then it becomes a worthy investment.

I thought this was a basic project management question about trading off quality, scope, schedule, staffing, etc. and offered the usual suggestions of cutting scope (nope CEO demands entire scope), cutting quality (nope can’t compromise quality), increasing the team, contracting out, reprioritizing and realeasing P1s in 2 weeks (nope, nope, nope). Obviously I didn’t get the job.

Later I learned from an acquaintance inside the company that the right answer was probably “Work the team 24 hour days, nights and weekends, for 2weeks to deliver what the CEO wants. I think I dodged a bullet there.