4 Practices to Lowering Your Defect Rate

Writing software is a battle between complexity and simplicity. Striking balance between the two is difficult. The trade-off is between long unmaintainable methods and too much abstraction. Tilting too far in either direction impairs code readability and increases the likelihood of defects.

Are defects avoidable? NASA tries, but they also do truckloads of testing. Their software is literally mission critical – a one shot deal. For most organizations, this isn’t the case and large amounts of testing are costly and impractical. While there is no substitute for testing, it’s possible to write defect resistant code, without testing.

In 20 years of coding and architecting applications, I’ve identified four practices to reduce defects. The first two practices limit the introduction of defects and the last two practices expose defects. Each practice is a vast topic on its own in which many books have been written. I’ve distilled each practice into a couple paragraphs and I’ve provided links to additional information when possible.

1. Write Simple Code

Simple should be easy, but it’s not. Writing simple code is hard.

Some will read this and think this means using simple language features, but this isn’t the case — simple code is not dumb code.

To keep it objective, I’m using cyclomatic complexity as a measure. There are other ways to measure complexity and other types of complexity, I hope to explore these topics in later articles.

Microsoft defines cyclomatic complexity as:

Cyclomatic complexity measures the number of linearly-independent
paths through the method, which is determined by the number and
complexity of conditional branches. A low cyclomatic complexity
generally indicates a method that is easy to understand, test, and
maintain.

What is a low cyclomatic complexity? Microsoft recommends keeping cyclomatic complexity below 25.

To be honest, I’ve found Microsoft’s recommendation of cyclomatic complexity of 25 to be too high. For maintainability and complexity, I’ve found the ideal method size is between 1 to 10 lines with a cyclomatic complexity between 1 and 5.

Bill Wagner in Effective C#, Second Edition wrote on method size:

Remember that translating your C# code into machine-executable code is a two-step process. The C# compiler generates IL that gets delivered in assemblies. The JIT compiler generates machine code for each method (or group of methods, when inlining is involved), as needed. Small functions make it much easier for the JIT compiler to amortize that cost. Small functions are also more likely to be candidates for inlining. It’s not just smallness: Simpler control flow matters just as much. Fewer control branches inside functions make it easier for the JIT compiler to enregister variables. It’s not just good practice to write clearer code; it’s how you create more efficient code at runtime.

To put cyclomatic complexity in perspective, the following method has a cyclomatic complexity of 12.

public string ComplexityOf12(int status)
{
    var isTrue = true;
    var myString = "Chuck";

    if (isTrue)
    {
        if (isTrue)
        {
            myString = string.Empty;
            isTrue = false;

            for (var index = 0; index < 10; index++)
            {
                isTrue |= Convert.ToBoolean(new Random().Next());
            }

            if (status == 1 || status == 3)
            {
                switch (status)
                {
                    case 3:
                        return "Bye";
                    case 1:
                        if (status % 1 == 0)
                        {
                            myString = "Super";
                        }
                        break;
                }

                return myString;
            }
        }
    }

    if (!isTrue)
    {
        myString = "New";
    }

    switch (status)
    {
        case 300:
            myString = "3001";
            break;
        case 400:
            myString = "4003";
            break;

    }

    return myString;
}

A generally accepted complexity hypothesis postulates a positive correlation exists between complexity and defects.

The previous line is a bit convoluted. In the simplest terms — keeping code simple reduces your defect rate.

2. Write Testable Code

Studies have shown that writing testable code, without writing the actual tests lowers the incidents of defects. This is so important and profound it needs repeating: Writing testable code, without writing the actual tests, lowers the incidents of defects.

This begs the question, what is testable code?

I define testable code as code that can be tested in isolation. This means all the dependencies can be mocked from a test. An example of a dependency is a database query. In a test, the data is mocked (faked) and an assertion of the expected behavior is made. If the assertion is true, the test passes, if not it fails.

Writing testable code might sound hard, but, in fact, it is easy when following the Inversion of Control (Dependency Injection) and the S.O.L.I.D principles. You’ll be surprised at the ease and will wonder why it took so long to start writing in this way.

3. Code Reviews

One of the most impactful practice a development team can adopt is code reviews.

Code Reviews facilitates knowledge sharing between developers. Speaking from experience, openly discussing code with other developers has had the greatest impact on my code writing skills.

In the book Code Complete, by Steve McConnell, Steve provides numerous case studies on the benefits code reviews:

  • A study of an organization at AT&T with more than 200 people reported a 14 percent increase in productivity and a 90 percent decrease in defects after the organization introduced reviews.
    • The Aetna Insurance Company found 82 percent of the errors in a program by using inspections and was able to decrease its development resources by 20 percent.

    • In a group of 11 programs developed by the same group of people, the first 5 were developed without reviews. The remaining 6 were developed with reviews. After all the programs were released to production, the first 5 had an average of 4.5 errors per 100 lines of code. The 6 that had been inspected had an average of only 0.82 errors per 100. Reviews cut the errors by over 80 percent.

If those numbers don’t sway you to adopt code reviews, then you are destined to drift into a black hole while singing Johnny Paycheck’s Take This Job and Shove It.

4. Unit Testing

I’ll admit, when I am up against a deadline testing is the first thing to go. But the benefits of testing can’t be denied as the following studies illustrate.

Microsoft performed a study on the Effectiveness on Unit Testing. They found that coding version 2 (version 1 had no testing) with automated testing immediately reduced defects by 20%, but at a cost of an additional 30%.

Another study looked at Test Driven Development (TDD). They observed an increase in code quality, more than two times, compared to similar projects not using TDD. TDD projects took on average 15% longer to develop. A side-effect of TDD was the tests served as documentation for the libraries and API’s.

Lastly, in a study on Test Coverage and Post-Verification Defects:

… We find that in both projects the increase in test coverage is
associated with decrease in field reported problems when adjusted for
the number of prerelease changes…

An Example

The following code has a cyclomatic complexity of 4.

    public void SendUserHadJoinedEmailToAdministrator(DataAccess.Database.Schema.dbo.Agency agency, User savedUser)
    {
        AgencySettingsRepository agencySettingsRepository = new AgencySettingsRepository();
        var agencySettings = agencySettingsRepository.GetById(agency.Id);

        if (agencySettings != null)
        {
            var newAuthAdmin = agencySettings.NewUserAuthorizationContact;

            if (newAuthAdmin.IsNotNull())
            {
                EmailService emailService = new EmailService();

                emailService.SendTemplate(new[] { newAuthAdmin.Email }, GroverConstants.EmailTemplate.NewUserAdminNotification, s =>
                {
                    s.Add(new EmailToken { Token = "Domain", Value = _settings.Domain });
                    s.Add(new EmailToken
                    {
                        Token = "Subject",
                        Value =
                    string.Format("New User {0} has joined {1} on myGrover.", savedUser.FullName(), agency.Name)
                    });
                    s.Add(new EmailToken { Token = "Name", Value = savedUser.FullName() });

                    return s;
                });
            }
        }
    }

Let’s examine the testability of the above code.

Is this simple code?

Yes, it is, the cyclomatic complexity is below 5.

Are there any dependencies?

Yes. There are 2 services AgencySettingsRepository and EmailService.

Are the services mockable?

No, their creation is hidden within the method.

Is the code testable?

No, this code isn’t testable because we can’t mock AgencySettingsRepository and EmailService.

Example of Refactored Code

How can we make this code testable?

We inject (using constuctor injection) AgencySettingsRepository and EmailService as dependencies. This allows us to mock them from a test and test in isolation.

Below is the refactored version.

Notice how the services are injected into the constructor. This allows us to control which implementation is passed into the SendMail constructor. It’s then easy to pass dummy data and intercept the service method calls.

public class SendEmail
{
    private IAgencySettingsRepository _agencySettingsRepository;
    private IEmailService _emailService;


    public SendEmail(IAgencySettingsRepository agencySettingsRepository, IEmailService emailService)
    {
        _agencySettingsRepository = agencySettingsRepository;
        _emailService = emailService;
    }

    public void SendUserHadJoinedEmailToAdministrator(DataAccess.Database.Schema.dbo.Agency agency, User savedUser)
    {
        var agencySettings = _agencySettingsRepository.GetById(agency.Id);

        if (agencySettings != null)
        {
            var newAuthAdmin = agencySettings.NewUserAuthorizationContact;

            if (newAuthAdmin.IsNotNull())
            {
                _emailService.SendTemplate(new[] { newAuthAdmin.Email },
                GroverConstants.EmailTemplate.NewUserAdminNotification, s =>
                {
                    s.Add(new EmailToken { Token = "Domain", Value = _settings.Domain });
                    s.Add(new EmailToken
                    {
                        Token = "Subject",
                        Value = string.Format("New User {0} has joined {1} on myGrover.", savedUser.FullName(), agency.Name)
                    });
                    s.Add(new EmailToken { Token = "Name", Value = savedUser.FullName() });

                    return s;
                });
            }
        }
    }
}

Testing Exmaple

Below is an example of testing in isolation. We are using the mocking framework FakeItEasy.

    [Test]
    public void TestEmailService()
    {
        //Given

        //Using FakeItEasy mocking framework
        var repository = A<IAgencySettingsRepository>.Fake();
        var service = A<IEmailService>.Fake();

        var agency = new Agency { Name = "Acme Inc." };
        var user = new User { FirstName = "Chuck", LastName = "Conway", Email = "chuck.conway@fakedomain.com" }

        //When

        var sendEmail = new SendEmail(repository, service);
        sendEmail.SendUserHadJoinedEmailToAdministrator(agency, user);


        //Then
        //An exception is thrown when this is not called.
        A.CallTo(() => service.SendTemplate(A<Agency>.Ignore, A<User>.Ignore)).MustHaveHappened();

    }

Closing

Writing defect resistance code is surprisingly easy. Don’t get me wrong, you’ll never write defect-free code (if you figure out how, let me know!), but by following the 4 practices outlined in this article you’ll see a decrease in defects found in your code.

To recap, Writing Simple Code is keeping the cyclomatic complexity around 5 and the method size small. Writing Testable Code is easily achieved when following the Inversion of Control and the S.O.L.I.D Principles. Code Reviews help you and the team understand the domain and the code you’ve written — just having to explain your code will reveal issues. And lastly, Unit Testing can drastically improve your code quality and provide documentation for future developers.

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