Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Computers don’t retire, overreact and complain. They could in minutes get all the knowledge accumulated by other computers. They could work 24 hours without making stupid mistakes. They make more and more human jobs obsolete. It is inevitable, computers will replace programmers in many areas. Even more, if Strong AI, capable of reasoning and understanding meaning, will appear, programming as a profession will be almost eliminated (at least coding part). Customers will be able to describe their needs directly to a computer. Computer AI will be translating these specifications to machine code (and stronger AI will require less formal specifications) and relentlessly building the software system.

Does it mean that that at the some point in the future software developers will no longer be needed? It could be true, if customers could specify exactly what they need and can effectively collaborate with AI to build the system. But things are not so simple, especially with non-trivial problems and humans (assuming that customers and users will be still humans). There are three roles that software specialists could play in the future even with powerful computer programming AI.

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Agile approaches insist that customer cannot come up with the reliable requirements at the beginning of the project. Martin Fowler provides four reasons:

1. Software development is a design activity, and thus hard to plan and cost.
2. The basic materials (technology and tools) keep changing rapidly
3. So much depends on which individual people are involved, and individuals are hard to predict and quantify.
4. In today’s economy the fundamental business forces are changing the value of software features too rapidly.

However, the most interesting reason is inside customers minds – they cannot reliably predict what they will need from software in the future.

Lets face inconvenient reality – most of the software ideas, decisions and requirements are not the result of scrupulous research, well prepared theories and mathematical models. If it were so, software development would take so much time, money and energy that it will almost eliminate any business benefits. Also it is very difficult to formalize anything related to irrational human beings (who accidentally are the main users of the software). In the most cases, software ideas are born from intuition, insights, copying other ideas and own experience mixed with some vague expectations. This empirical mess becomes source of the vision, requirements and future problems.

Customer dissatisfaction with software is the problem number one in software development. And the root cause is that many of customer’s expectations and predictions are in vain – mostly because future is fundamentally different than it appears in their thoughts. Why? There are six challenges that customers are facing to predict the future and get their ideas about the software right.

1. Subjectivity and our individual experience

alikaragoz
Software ideas are mainly based on our individual experiences, thoughts and understanding. Our individual experience is unobservable to everyone except ourselves. It is very difficult to communicate it right and completely understand other people. Translations and interpretations of software needs are unreliable – too much depends on what experiences we had, our memory and level of knowledge.
We see problems and possible solutions through lenses of our individual experience and often they shape and distort what we see. In addition, even if software customers can get objective view on their problems, they would see it from completely different perspective than domain experts, developers and end-users – people who shape ideas, build and use software. Subjectivity makes expression and understanding of software ideas very difficult.

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Surprisingly, Jim Highsmith, respected agile guru, argues against one of the core agile principles of self organization, associating it with anarchy. On the contrary, surprisingly again, the Army experts, Don Vandergriff and George Reed, embrace ideas of adaptability and decentralization in traditionally command-oriented military units.

Jim Highsmith says:

“I’ve been thinking recently that the term “self-organizing” has outlived its usefulness in the agile community and needs to be replaced. While self-organizing is a good term, it has, unfortunately, become confused with anarchy in the minds of many. Why has this occurred? Because there is a contingent within the agile community that is fundamentally anarchist at heart and it has latched onto the term self-organizing because it sounds better than anarchy.”

Army experts say:

“A culture of adaptability is one that accepts a lack of absolute control over events on and off the battlefield. Implementation requires revisiting mission orders or trust tactics. It necessitates raising the bar in the education, training and coaching of leaders and soldiers. It seems trite to suggest that an adaptive institution will reward those who, when the need arises, act without waiting for orders, but this also necessitates a climate that is supportive of those who act and fail to achieve stellar results. Instead of seeking perfection or optimum solutions, operators will find a solution that works locally and then exploit those results as a continual evolution facilitated by an organization adept at receiving and communicating such information.”

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Part 1. Gaining processing power
Part 2. Becoming intelligent
Part 3. Interacting with humans
Part 4. Building useful programs
Part 5. Future of human programmers

Is it easy to build useful programs for humans? Failure rates and dissatisfaction with the software projects (more than 50% still fails or challenged) show that it is not quite easy task. Can AI help to build more successful projects, compete and eventually replace human programmers?

From ideas to specifications

Human programmers face objective challenges in building software systems, which AI will face in the future:

1. It is difficult to understand what people need.
2. Customer’s ideas are shifting, once they start using the real system and experience all the consequences and effects for interacting with it in the context of their problems.
3. Customer’s needs are changing constantly reflecting outside business and company trends, situation and problems.

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