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When am I done
A rough estimate puts the costs of software-related downtime at about US$300 billion annually.(1) Consider these examples:
A telecommunications provider spends US$3 million on software application • support over six months instead of on new application development.
A health insurance company loses US$20 billion annually in lost business • and repair work related to production performance problems.
Research and experience have shown that the cost of finding and removing defects grows exponentially with the quality of the code. That is, as each defect is removed, the cost of finding and removing the next defect increases. Hence, it is a practical impossibility to deliver zero-defect software. C-level executives understandably want products whose defects have a minimal impact on users — either functionally or financially. But such certainty — and the enhanced ability to avoid disasters like those described above — costs money. Hundreds of millions of dollars are spent yearly on software testing. Indeed, IBMâ€™s direct experience with organizations worldwide and data gathered by researchers working with hundreds of companies show that most firms invest 25 percent or more of their development lifecycle time and cost in quality assurance.
A key point in any development process, therefore, is the point at which testing ends and the organization moves ahead with deployment. This is the time in the life of every development project when the program manager has to ask the very practical questions:
This white paper provides an innovative framework for answering those questions — a new barometer for measuring the business risk of release versus the cost of continued testing.
Framing the right question — technical versus economic risk
Upon initial examination, the question of when to stop testing appears to be solely technical. Organizations commonly apply exit criteria for the testing process based on factors such as the percentage of successful tests for completeness of functionality and the number of defects remaining at various severity levels. Some quality assurance teams have also measured quality using metrics in areas such as:
These technical testing measures are critical, but they are often blindly applied. Applied without a larger context, they can miss key economic implications inherent in the decision to continue testing or to release the software.
Instead of applying only technical criteria, the decision to release must also consider the timing of the window of opportunity that surrounds the softwareâ€™s release date. Late release can mean lost revenue or lost efficiencies. However, early release can mean risk to the business in damaged reputation, organizational disruption and high service costs. To find this balance, companies should consider a number of factors, including:
Getting started with a basic formula
Suppose that for a given software build it is possible to approximate the companyâ€™s loss in revenue and maintenance to fix a software failure. Call that the expected cost avoidance (ECA). With more testing, one would expect the ECA to decrease. If not, why spend money on testing?
However, with more testing, the release date moves out, resulting in lost benefit to the business. So we also need to measure this monetary lost benefit. Call that the expected value lost (EVL). EVL can increase with new functionality but decrease with time to release.
Early in the development cycle, the ECA presumably is much higher than the EVL. The product does not do much, but it would generate lots of defects to fix. As a result, the expected business risk (EBR), calculated as EBR = ECA - EVL, is a large positive number. Eventually, with good execution, the EBR should go to zero or even turn negative.
Even so, the question remains: "Does the reduction in EBR justify further testing?" Call the cost of testing, CT. As the ratio of EBR/CT approaches 1 or less, it becomes apparent that the investment in further testing is not yielding any more value and might even be counterproductive.
Analyzing usage for a more precise answer
The above reasoning presupposes that we can actually determine EBR. As we will show, there are practical, if not simple, methods to compute business risk in terms of ECA and EVL. To apply the reasoning, it is necessary to thoroughly analyze the system usage to establish the right test cases that not only provide sufficient usage and code coverage, but also test those other considerations such as security, performance and reliability that have economic impact after deployment. Test planning, as a result, should include both technical and economic concerns.
Determining economic risks from quality concerns
When considering the economic risks and value of deployment, a useful starting point is the common set of quality concerns known as FURPS — features, usability, reliability, performance and supportability:
Each of these concerns has both technical specification and economic implications. It is beyond the scope of the paper to fully address them all. Rather, we can apply the reasoning to reliability to establish the pattern that, with adjustment, can be equally applied to other concerns. Therefore, we start with the questions: "What does reliability look like to the business?" and "What is the cost of a system failure?"
The equation applied to reliability testing
The equation for reliability testing has two aspects — determining of ECA and determining EVL. In the sections that follow, we evaluate both issues.
Using the failure density function (fdf) to determine ECA
To measure the statistical impact to business risk in terms of ECA, some theory is needed to extrapolate the failure rate beyond practical testing time. This theory is available using the failure density function (fdf), which is described by a statistical distribution. Unlike the bell curve that describes normal distribution, the fdf distribution for likelihood of failures over time (6) is:
-λt for t ≥ 0
= 0 for t< 0
The graph of fdfâ€™s for different build λâ€™s is given by figure 1 below, where the x axis shows time and the y axis shows the density of failures at a given time. Note that the probability of a failure before a given time (t) is given by the area under the curve from 0 to t. Applying some first-year calculus results in the following:
where P[0,t] is the likelihood of failure before time t.
Testing and repair should decrease the λ — in effect flattening and pushing to the right the failure density function distribution. Decreasing λ then means the failures are more likely to arise further in the future, making the program more reliable.
To get an approximate λ for a given build:
Create a histogram of the times
to failure. Normalize the histogram by dividing each of its values by
the number of tests. This results in a table showing percentage of
failure rates over time.
Curve-fit the table to get an fdf distribution.
Once λ is known, it is possible to estimate the likelihood of failure between time 0 and any time t by computing the area under the curve from 0 to t. Note this is true even if the time is far in the future.
From each of the buildfdf distributions, we can apply the business loss probability and maintenance impact measures to get the ECA.
Finding the value of software at release — determining EVL
To measure the EVL, the organization must have some idea of the value of the program at release. This value generally is time dependent — that is, the later the release, the less the program is worth. Some examples include:
In the latter case, data must be provided by the marketing department, as there are other ways in addition to timing that the system might deliver value.(7) In any case, a monetized value at delivery is necessary to making the "go/no-go" decision regarding testing.
Instrumenting the equation for reliability
The monetized business losses that result from reliability failures are directly related to the sort of software or system under development and the industry for which it is being developed. The starting point, therefore, is to ask the question, "What does reliability look like to the business?"
A simple example is one that many individuals and businesses encounter daily: a laptop operating system (OS) is often restarted after several days of using many software programs simultaneously to avoid abnormal behaviors such as slow performance, screen errors or even locked screens that may occur later as a result of continuous use. The developer of the OS knows that the cost of failure is low enough and that the OS meets standards of acceptable reliability if the MTTF is greater than the acceptable time between restarts.
Another example occurs less frequently and can be more costly: If the system is an office telephone switch, each failure may cost tens of thousands of dollars. In this case, the likelihood of a single failure in a year needs to be less than 10-3 or 10-4.
Using the failure density function analysis described earlier, we now examine an example from the IBM Research Lab to substantiate the premise.
Measuring reliability as time to failure
Reliability is measured as the time between failures during testing and field operations. To test reliability, a large number of simultaneous test runs for a series of software builds is necessary under a variety of loads and random functions. The time to failure can vary with each build. Thus for each build we can create a histogram of time to failure. Each build has a different histogram.
For example, a given build may use automated testing to run 10,000 reliability tests. Each test case launches the build into a parallel process. The number of processes that crashed during a dayâ€™s run are counted and recorded. The whole experiment is observed for a period of four days as illustrated in this table:
Although reliability experiments ran for only four days (and some runs did not fail in the time allotted), it is possible to extrapolate and compute the probability of failure for the system for any interval of future time.
Applying the extrapolation process to the data in table 1 results in the following graph:
For build 1, λ = 2.63 × 10-3, and the chance of system failure for a continuous period of up to one month is determined by computing the area under the extrapolated curve according to the formula F(t) = 1 - e-λt = 99.96%. Note that it is possible to estimate this chance of failure even though the test may not last for the full one-month period.
Note also that additional measures of the reliability improvements from testing and repair activity can be measured by various statistical measures, e.g., the mean of the buildfdf distributions. For example, the MTTF is the mean of the fdf distribution for a given build. A computation of MTTF for builds 1-5 is shown in table 2.
Measuring business risk
Now letâ€™s consider the economic risks of testing. Once we know the likelihood of failure in any time period, we can also estimate the costs of addressing those failures. These costs depend on a variety of business variables such as the cost of labor and the purpose of the system. For each build, we need to estimate the following:
The business losses bl0, bl1 and maintenance expenses m0, m1 at times t0 and t1 are random variables, because they can only be estimated. With an assumed business loss basis of US$20 million, multiplying US$20 million by the probability of failure at the interval of time provides the bl variable. The maintenance stream is also provided as an estimated assumed set of variables.
To continue the example, by adding the expected (i.e., mean) maintenance and business loss expenses, we can expand table 2 to table 3.
From these random variables, it is possible to determine the expected losses due to failures in reliability. For increased testing, the ECA is determined by:
1 + m1 ) - mean (bl0 + m0)
This data suggests that we may have finished testing, but we cannot be sure. The economic benefits and risk reduction of testing are leveling off, but they are not flat. We also need to consider how much benefit in terms of new revenue is lost by postponing release. To do that we need to consider the value of the system itself for each build.
Because the system or software may deliver value over some future interval of time, the net present value (NPV)—which we will refer to asnet present revenue (NPR) of the system or software at delivery — must be computed. A useful form of the equation for this purpose is:
The important consideration for the NPR of the system is that it tends to decrease with the delay in delivery but might increase with more functionality. Considering NPR allows us to balance cost avoidance achieved by increasing testing with the loss of value from delays due to testing.
Returning to our example, if we are told by the marketing department that the Ri for quarterly estimates is US$5 million, and rR = 6%, NPR for the system at different delivery dates is shown in table 4.
Similarly, the EVL due to missed opportunities during the testing period is determined by:
EVL= mean(NPR0 ) - mean(NPR1 )
The overall EBR of increased testing is:
EBR = ECA - EVL
For builds 2-5 of the example system, the ECA, EVL and EBR computation, therefore, is as follows:
Note that the EBR turns negative at build 5. This certainly suggests it is time to stop testing. In the next section, we explore the question further.
Criteria for stopping tests — business risk versus cost of testing
With this framework in place, the stage is set to answer the question, "When am I done testing?" Depending on the depth of understanding of the risks and values, one might apply a statistical approach to the random variables to get the answer. To keep the answer as simple as possible, we continue to look solely at the means.
Here are two ways to answer the question:
1. At a certain point in the process, the EBR may turn negative. At this point the lost opportunity revenue exceeds the costs being avoided, so it is reasonable to go ahead and ship.
2. Given the CT, it is possible to determine the expected value of testing (EVT) as a savings-to-cost ratio:
EVT = EBR/CT
A value less than 1 indicates that the organization is spending more than it is saving—whereas a value greater than 1 indicates a savings due to increased testing. Therefore, in table 6 it would appear that testing might stop at build 5, where EVT < 0.
With the EVT calculation, we have completed the economic analysis. We now find that in this example we are losing more money in the cost of testing and missed potential revenue than we are potentially saving by avoiding the risk of failure. So now we need to ask ourselves, "Are there other reasons for creating another build?" This is not a mechanistic decision, but the data does help guide our thinking. Other factors such as the corporate culture determine the next step. Decision makers may want to elevate their confidence levels with one more build.
Quality governance: organizational and decision concerns
Finally, it also is important to consider the quality governance aspects of the decision to declare testing "done." Governance is about deciding the "who, what, when, why and how" of decision making.(8) Table 7 summarizes the factors to consider for each aspect of the testing governance model.
Because the quality of software can affect the entire business, the issue of quality governance often involves C-level and director-level executives. It may be time, however, to consider two new roles that do not exist in most organizations—a chief quality officer and a quality assurance statistician.
Today quality assurance teams focus on testing and provide resulting FURPS validation for use in decision making. But someone, a quality "czar" with specific responsibilities for full lifecycle quality management of business-critical software, needs to drive the overarching quality strategy and decision-making criteria for the business.
In addition, traditional quality assurance teams do not have anyone on staff with the mathematical background to perform the analysis discussed in this paper. A dedicated quality assurance statistician, however, can provide the detailed analysis required for informed "go/no-go" decisions.
Finally, these calculations would provide a basis for governance decisions that follow software deployment. For example, MTTF and probability data from reliability testing for failure can be extremely useful in planning software and system maintenance cycles. Aircraft, for example, have cyclical maintenance periods based on such data. Failure testing may also reveal useful information to include in end-user documentation for troubleshooting. Failures related to scalability performance can become the basis for production monitoring decisions
Deciding when testing is complete is both an economic and a technical issue. Common FURPS-based testing provides significant insight into the needs, the process and the status of technical results. But for a true understanding of new softwareâ€™s readiness, the business impact of shipping now versus further testing and repair must be considered.
We have shown that the techniques that provide the required insights are available. Not surprisingly, they do require some analytical capability and a new consideration in the governance of the release process. However, by adopting these methods and release governance, the companyâ€™s management team can achieve a firm, economic basis for answering an important and very fundamental development question, -- "When am I done testing?"
1The Standish Group, Comparative Economic Normalization Technology Study, CHAOS Chronicles v12.3.9, June 30, 2008.
2 "FAA Computer Glitch Causes Flight Delays," Tescom, http://www.tescom-intl.com/site/en/tescom.asp?pi=465&doc_id=2923
3 & 4Forrester Research, "Performance-Driven Software Development," Carey Schwaber, February 2006.
5Walker Royce, Software Project Management: A Unified Framework, (Addison-Wesley Professional, Indianapolis, 1998)
6M. Modarres, M. Kaminskiy and V. Krivtsov, Reliability Engineering and Risk Analysis: A Practical Guide (Marcel Dekker, Inc., New York, 1998).
7M. Cantor, "The Value of Development," article in preparation.
8M. Cantor and J. Sanders, "Operational IT Governance," IBM developerWorks®, May2007
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