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In spite of what your client may tell you, there's always a problem.

-- Gerald Weinberg

Software is a scarce resource, in that the demand for software greatly outstrips the supply. We hear about huge shortages of IT staff required to meet to this demand. Costs are rising, too. Some people believe the way we can increase output is to outsource development to places where qualified labor is cheap and plentiful. However, the problem with software development lies elsewhere, and increasing the number of programmers and separating them from the customer only makes the problem worse.

A programmer's job is getting the details exactly right, exactly once. This isn't at all like physical manufacturing where the brunt of the cost is in the process of making the exact copies of a product. Outsourced manufacturing works well, because the details have already been decided in the design phase. The manufacturing process merely replicates this fixed design. With software, the cost of making copies is almost free, and it's the efficiency of design phase that governs its cost. Cheap and abundant labor improves manufacturing efficiency, but this economy of scale does not make software development more efficient.

The cost of programming is directly related to network effects. As you add programmers to project, the communication costs increase proportionally with the square of the total number of programmers. There are that many more links over which the details must be communicated. And, as the customer and programmers drift farther apart, the cost of the most important link increases. Reducing the cost of communication between the programmers and the customer is crucial to getting the details right efficiently. A time lag along this link multiplies the cost. To improve efficiency, the customer needs instantaneous communication with the programmers, and programmers need immediate feedback from the customer.

This chapter differentiates software development from physical manufacturing. We explain why traditional, plan-driven development methodologies increase project risk, and how fostering failure reduces risk. The chapter ends with a parable that shows the way to reduce both requirements and implementation risk is to bring the customer closer to development.

Some Statistics

According to the Business Software Alliance, the software industry is growing rapidly to meet a seemingly unlimited demand. From 1990 to 1998, the U.S. software industry's revenue grew by 13% per year.[1]

Despite, or perhaps as a result of this rapid growth, the software industry remains highly inefficient. While sales grew by 18% in 1998, an astounding 26% of U.S. software projects failed and another 46% were labeled as challenged by the Standish Group International.[2] They also estimate 37% of our resources are wasted on failed and challenged software projects.

We need to understand why software development is so inefficient and why projects fail.

Risk Averse Methodologies

It's not that failure is all bad. People learn from mistakes. However, we don't want to be driving on the mistake bridge builders learn from. We don't want to be flying in an engineering error, to live near a nuclear plant failure, or even to stand near a pancake griddle meltdown.[3]

To reduce risk, engineering methodologies are plan-driven. The plans help us ensure we catch mistakes as early as possible. The planning process involves many redundant steps. Emerging plans must pass reviews and consistency checks during the numerous phases of the project. The public is protected by layers of methodology and in some cases government regulations and laws.

Although public safety is certainly a concern, business probably evolved these risk mitigation methodologies for another reason: to reduce the risk of production failures. When you are manufacturing physical widgets, you don't want to find an error after you have produced one million widgets, or even a thousand. The cost of the raw materials plus the time to fix the error, to retool, and to rerun the job is usually high in comparison to the cost of the extra procedures to catch errors during the planning and design phases.

Software development is quite different from manufacturing. The cost of producing the physical software package is nominal, especially considering most software is developed for only one customer.[4] Today, automated updates via the Web further reduce the cost of software delivery. The cost of software production is borne almost entirely by research, development, and maintenance.

While software lacks the characteristics of physical products, we still develop most software with the same implementation risk averse methodologies. We are told "If [a requirements] error is not corrected until the maintenance phase, the correction involves a much larger inventory of specifications, code, user and maintenance manuals, and training material."[5] Mistakes are expensive, because we have "inventory" to update. Plan-driven software development is firmly grounded in avoiding production failures, which slows development in the name of implementation risk mitigation.

Fostering Failure

Implementation risk mitigation is expensive. The most obvious cost is the bookkeeping material (documents defining requirements, specifications, architecture, and detailed design) in addition to the code we need to maintain. Less risk averse methodologies lower the cost of software production. Reducing redundancy in the planning process means there is less to change when a requirements error is inevitably discovered. By not creating inventory in the first place we further reduce our overhead and inefficiencies.

When we improve efficiency in one part of the process, we gain flexibility in other areas. We have more resources and time to correct errors in all phases of the project. The fewer errors, the better the chance the project will succeed.

Implementation risk aversion is costly in other ways. We avoid change later in the project even if that change is justified. The cost of change is proportional to the amount of inventory. In plan-driven methodologies, change is increasingly costly as the project progresses. Not only do we have to update all the bookkeeping material, but it must pass the same manual reviews and consistency checks that were used to validate the existing plan and design.

And possibly the most important cost is risk aversion itself. Failure is a natural part of creation. We don't like to fail, but when we do, we usually learn from the experience. According to management gurus Jim Collins and Jerry Porras, "What looks in retrospect like brilliant foresight and preplanning was often the result of 'Let's try a lot of stuff and keep what works.'"[6]

An interesting side-effect of reducing the cost of correcting errors is that we reduce the risk associated with trying new and innovative solutions.

Get Me a Rock

Reducing the cost of correcting errors is one part of the problem. One reason projects fail is that they do not satisfy the end-users' needs. To help ensure a project's success, we need to mitigate requirements risk. The following story about a manager and his subordinate demonstrates the difficulty of specifying and satisfying requirements:


Boss: Get me a rock.
Peon: Yes, sir.

...a little while later...


Peon: Here's your rock, sir.
Boss: This rock is all wrong.  We need a big rock.

...another delay...


Peon: Here ya go, boss.
Boss: We can't use this rock.  It's not smooth.

...yet another delay...


Peon: [panting] Smooth, big rock, sir.
Boss: The other rocks you brought were black,
but this one's brown. Get a black one.

And the story goes on and on. We've all been there. Both roles are difficult. It is hard to specify exactly what you want when you're not sure yourself, or even when you are sure, you may have difficulty explaining to another person what you want. On the flip side, the subordinate probably doesn't speak the language of rocks, so he can't elicit what the manager wants in terms the manager understands.

The plan-driven lesson to be learned is: Customers must give precise instructions (specifications). Programmers should not be expected to be mind readers.

Requirements Risk

Most software projects are as ill-defined as the requirements in this story.[7] The plan-driven approach is to spend a lot of time up front defining the requirements in order to reduce the cost of the implementation. The theory is that planning is cheap, and programming is expensive. Once we get through the specification phase, we can ship the spec off to a source of cheap labor whose job it is to translate the spec into working code. That would work fine if the specification were exactly right, but it most likely is missing a lot of important detail, and the details it identifies probably aren't exactly right either. The Rock example doesn't do justice to the amount of detail involved in software. Large programs contain hundreds of thousands and sometimes millions of details that must be exactly right, or the software contains faults.

The cumulative effect of software faults is what causes projects to fail. It's easy to fix a few faults but not thousands. When users throw up their hands and scream in exasperation, they're saying the program misses the mark by a mile. It's insufficient to tell them the specification was right or that the programmers simply misunderstood it. It's the code users are frustrated with, and it's the code that is just plain wrong.

Planning and specification does not guarantee end-user satisfaction. Plan-driven methodologies ignore requirements risk, that is, the risk that details may be incorrect, missing, or somehow not quite what the customer wants. When we gather requirements, write the specification, ship it off, and only check the program against user expectations at the end, we are setting ourselves up for failure. Requirements change in this scenario is very expensive. This is what we see in the Rock example. The requirements risk is proportional to this time lag. Given the predominance of plan-driven software development, it's likely that a large number of project failures are directly attributable to too little requirements risk mitigation.

Let's Rock And Roll

Fortunately, there is an alternative version of the Get Me a Rock story, which solves the ill-defined requirements problem with greater efficiency:


Boss: Get me a rock.
Peon: Sure, boss.  Let's go for a ride to the quarry.

...a little while later...


Boss: Thanks for pointing out this rock.
I would have missed it if I went by myself.
Peon:You're welcome, boss.

The moral of this story is: to increase efficiency and quality, bring the customer as close as possible to a project's implementation.

Footnotes

  1. Business Software Alliance, Forecasting a Robust Future: An Economic Study of the U.S. Software Industry, Business Software Alliance. June 16, 1999. http://www.bsa.org/usa/globallib/econ/us_econ_study99.pdf

  2. The Standish Group conducted a study of 23,000 software projects between 1994 and 1998. Failed means "The project was canceled before completion." Challenged means "The project is completed and operational, but over-budget, over the time estimate and with fewer features and functions than initially specified." See CHAOS: A Recipe for Sucess, The Standish Group International, Inc., 1999.

  3. Our breakfast suddenly turned into splattered, molten metal one Sunday. Fortunately, no one was hurt.

  4. The Business Software Alliance report estimates 64% of software sales is in the customized software and integrated system design services. This does not include internal IT budgets.

  5. Software Engineering Economics, Barry Boehm. Prentice-Hall, Inc. 1981, pp. 39-40. This classical reference is old but unfortunately not outdated, viz., Get Ready for Agile Methods with Care, Barry Boehm. IEEE Software. Jan, 2002, pp. 64-69.

  6. Built to Last, Jim Collins and Jerry Porras, HarperBusiness. 1997, p. 9.

  7. On page 310 of Software Engineering Economics, Barry Boehm states, "When we first begin to evaluate alternative concepts for a new software application, the relative range of our software cost estimates is roughly a factor of four on either the high or low side. This range stems from the wide range of uncertainty we have at this time about the actual nature of the product."

 
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