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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.

Index Fragmentation in SQL Azure, Who Knew!

I’ve been on my project for over a year and it has significantly grown as an application and in data during the year. It’s been nonstop new features. I’ve rarely gone back and refactored code. Last week I noticed some of the data heavy pages were loading slowly. At the worst case one view could take up to 30 seconds to load. 10 times over my maximum load time…

Call me naive, but I didn’t consider index fragmentation in SQL Azure. It’s the cloud! It’s suppose to be immune to premise issues… Apparently index fragmentation is also an issue in the cloud.

I found a couple of queries on an MSDN blog, that identify the fragmented indexes and then rebuilds them.

After running the first query to show index fragmentation I found some indexes with over 50 percent fragmentation. According to the article anything over 10% needs attention.

First Query Display Index Fragmentation

--Get the fragmentation percentage

SELECT
 DB_NAME() AS DBName
,OBJECT_NAME(ps.object_id) AS TableName
,i.name AS IndexName
,ips.index_type_desc
,ips.avg_fragmentation_in_percent
FROM sys.dm_db_partition_stats ps
INNER JOIN sys.indexes i
ON ps.object_id = i.object_id
AND ps.index_id = i.index_id
CROSS APPLY sys.dm_db_index_physical_stats(DB_ID(), ps.object_id, ps.index_id, null, 'LIMITED') ips
ORDER BY ps.object_id, ps.index_id

Second Query Rebuilds the Indexes

--Rebuild the indexes
DECLARE @TableName varchar(255)

DECLARE TableCursor CURSOR FOR
(
 SELECT '[' + IST.TABLE_SCHEMA + '].[' + IST.TABLE_NAME + ']' AS [TableName]
 FROM INFORMATION_SCHEMA.TABLES IST
 WHERE IST.TABLE_TYPE = 'BASE TABLE'
 )

 OPEN TableCursor
 FETCH NEXT FROM TableCursor INTO @TableName
WHILE @@FETCH_STATUS = 0

 BEGIN
 PRINT('Rebuilding Indexes on ' + @TableName)
Begin Try
 EXEC('ALTER INDEX ALL ON ' + @TableName + ' REBUILD with (ONLINE=ON)')
End Try
Begin Catch
 PRINT('Cannot do rebuild with Online=On option, taking table ' + @TableName+' down for douing rebuild')
 EXEC('ALTER INDEX ALL ON ' + @TableName + ' REBUILD')
 End Catch
FETCH NEXT FROM TableCursor INTO @TableName
END

CLOSE TableCursor
DEALLOCATE TableCursor

Source

Proofing a Concept and Growing the Code

In a recent conversation, a friend mentioned he creates proof of concepts and then discards them after testing their viability. I’ve done the same in the past. This time it didn’t feel right. I cringed when he said he threw away to the code. Maybe my days as a business owner has turned me into a froogle goat, but it felt like he was throwing away value.

Why don’t we continue forward with a proof of concept?

Generally when I think of a proof of concept its hastily assembled. Many of the “best practices” are short-cutted if not downright ignored. The goal is to test the feasibility an idea. At some point you’ll realize if the solution will work. Then you’ll decide if it’s time to walk away from the idea and ditch the proof of concept or move forward with the idea. If you move forward with the idea, why not keep coding and turn the proof of concept into the real deal?

I’ll be honest here, it seems ridiculous that you’d create a solution and then throw it away just to create it again. That’s like poorly painting an entire house just to see if you like the color. “Yep, the color is good. Let’s paint the house for reals this time and this time we’ll do a good job.

There is another way. Evolve the code. Add in the missing infrastructure. This has the possibility of growing into a long term healthy solution.

Walking away from a proof of concept costs you value (time and money) that might otherwise be captured. Even if you don’t capture 100%, you’ll still be better off than just chucking everything and walking away. So next time, give it a try. See if you can morph a proof of concept into a sustainable project. I think you might be surprised at the end result.

Securing AngularJS with Claims

At some point an application needs authorization. This means different levels of access behave differently on a web site (or anything for that matter). It can be anything from seeing data to whole area’s that are not accessible by a group of users.

In non Single Page Applications (SPA), a claim or role is associated with data or an area of the application, either the user has this role or claim or he does not. In a SPA it’s the same, but with a huge disclaimer. A SPA is downloaded to the browser. At this point the browser has total control over the code. A nefarious person can change the code to do his bidding.

Because SPAs can’t be secured, authentication and authorization in a SPA is simply user experience. All meaningful security must be done on the web server. This article does not cover securing your API against attacks. I recommend watching a video from Pluralsight or reading a paper that addresses security for your server technology.

The intent of this article to show you how I added an authorization user experience to my Angular 1.x SPA.

Security Scopes

I have identified 3 areas of the UI that need authorization: Elements (HTML), Routes, and Data.

Just a reminder, securing a SPA is no substitute to securing the server. Permissions on the client is simply to keep the honest people honest and to provide the user with a good experience.

The 3 areas in detail:

Elements

You’ll need to hide specific HTML elements. It could be a label, a table with data, a button, or any element on the page.

Routes

You’ll want to hide entire routes. In certain cases you don’t want the user accessing a view. By securing the route a user can’t to navigate to the view. They instead will be shown a “You are not authorized to navigate to this view” message.

Data

Sometimes hiding the elements in the view is not enough. An astute user can simply view the source and see the hidden data in the HTML source or watch it stream to the browser. What we want is the data not to be retrieve in the first place..

Adding security is tricky. At first I tried constraining the access at the HTTP API (on the client). I quickly realized this wouldn’t work. A user might not have direct access to the data, but this doesn’t mean they don’t indirectly access to the data. At the HTTP API layer (usually one of the lowest in the application) we can’t tell the context of the call and therefore can’t apply security concerns to it.

Below I have provided coding samples:

Code

I created a service for the authorization checking code. This is the heart of the authorization. All authorization requests use this service to check if the user is authorized for the particular action.

angular.module('services')
    .service('AuthorizationContext',function(_, Session){

        this.authorizedExecution = function(key, action){

            //Looking for the claim key that was passed in. If it exists in the claim set, then execute the action.
            Session.claims(function(claims){
                var claim = findKey(key, claims);

                //If Claim was found then execute the call.
                //If it was not found, do nothing
                if(claim !== undefined){
                    action();
                }
            });
        };

        this.authorized = function(key, callback){
            //Looking for the claim key that was passed in. If it exists in the claim set, then execute the action.
            Session.claims(function(claims){
                var claim = findKey(key, claims);

                //If they don't have any security key, then move forward and authorization.
                var valid = claim !== undefined;
                callback(valid);
            });
        };

        //this.agencyViewKey = '401D91E7-6EA0-46B4-9A10-530E3483CE15';

        function findKey(key, claims){
            var claim = _.find(claims, function(item){
                return item.value === key;
            });

            return claim;
        }
    });

Authorize Directive

The authorize directive can be applied to any HTML element that you want to hide from users without a specific level of access. If the user has the access token as part of their claims they are allow to see the element. If they don’t it’s hidden from them.

angular.module(directives')
    .directive('authorize', ['$compile', 'AuthorizationContext', function($compile, AuthorizationContext) {
        return {
            restrict: 'A',
            replace: true,
            //can't have isolated the scope in a shared directive
            link:function ($scope, element, attributes) {

                var securityKey = attributes.authorize;
                AuthorizationContext.authorized(securityKey, function(authorized){
                    var el = angular.element(element);
                    el.attr('ng-show', authorized);

                    //remove the attribute, otherwise it creates an infinite loop.
                    el.removeAttr('authorize');
                    $compile(el)($scope);
                });
            }
        };
    }]);

Elements

I rely heavily on tabs in my application. I apply the authorize directive to the tab that I want to hide from users without the proper claims.

<tabset>
<tab ng-cloak heading="Users" authorize="{{allowUserManagement}}">
...html content
</tab>
</tabset>

Routes

I’m using the ui-router. Unfortunately for those who are not, I don’t have code for the out of the box AngularJS router.

In the $stateChangeStart I authenticate the route. This is the code in that event.

$rootScope.$on("$stateChangeStart", function(event, toState, toParams, fromState, fromParams){
   AuthenticationManager.authenticate(event, toState, toParams);
});

The function that authorizes the route. If it’s authorized, the route is allowed to continue. If it’s not authorized, a message is displayed to the user and they are directed to the home page.

function authorizedRoute(toState, location, toaster, breadCrumbs){
   if(toState.authorization !== undefined){
       AuthorizationContext.authorized(toState.authorization, function(authorized){
           if(!authorized){
               toaster.pop('error', 'Error', 'You are not authorized to view this page.');
               location.path("/search");
           } else {
               breadCrumbs();
           }
       });
   } else{
       breadCrumbs();
   }
}

In this router definition you’ll notice a property called ‘authorization’. If the user has this claim they are allowed to proceed.

angular.module('agency',
    [
        'ui.router',
        'services'
    ])
    .config(function config($stateProvider){
    $stateProvider.state( 'agency', {
        url: '/agency',
        controller: 'agency.index',
        templateUrl: 'agency/agency.tpl.html',
        authenticate: true,
        authorization:'401d91e7-6ea0-46b4-9a10-530e3483ce15',
        data:{ pageTitle: 'Agency' }
    });
});

Data

In some cases, you don’t want to make a request to the server for the data. If the user has the claim they’ll be allowed to make the request.

The above AuthorizationContext at beginning of the article show the code for authoriedExecution. Here you see it’s usage.

AuthorizationContext.authorizedExecution(Keys.authorization.allowUserManagement, function(){
    //execute code, if the loggedin user has rights.

                });

As I mentioned above, this is no substitute for securing the server. This code works for providing a wonder user experience.

5 Steps for Coding for the Next Developer

Most of us probably don’t think about the developer who will maintain our code. Until recently, I did not consider him either. I never intentionally wrote obtuse code, but I also never left any breadcrumbs.

Kent Beck on good programmers:

Any fool can write code that a computer can understand. Good programmers write code that humans can understand.

Douglas Crockford on good computer programs:

It all comes down to communication and the structures that you use in order to facilitate that communication. Human language and computer languages work very differently in many ways, but ultimately I judge a good computer program by it’s ability to communicate with a human who reads that program. So at that level, they’re not that different.

Discovering purpose and intent is difficult in the most well written code. Any breadcrumbs left by the author, comments, verbose naming and consistency, is immensely helpful to next developers.

I start by looking for patterns. Patterns can be found in many places including variables names, class layout and project structure. Once identified, patterns are insights into the previous developer’s intent and help in comprehending the code.

What is a pattern? A pattern is a repeatable solution to a recurring problem. Consider a door. When a space must allow people to enter and to leave and yet maintain isolation, the door pattern is implemented. Now this seems obvious, but at one point it wasn’t. Someone created the door pattern which included the door handle, the hinges and the placement of these components. Walk into any home and you can identify any door and it’s components. The styles and colors might be different, but the components are the same. Software is the same.

There are known software patterns to common software problems. In 1995, Design Patterns: Elements of Reusable Object-Oriented Software was published describing common software patterns. This book describes common problems encountered in most software application and offered an elegant way to solve these problems. Developers also create their own patterns while solving problems they routinely encounter. While they don’t publish a book, if you look close enough you can identify them.

Sometimes it’s difficult to identify the patterns. This makes grokking the code difficult. When you find yourself in this situation, inspect the code, see how it is used. Start a re-write. Ask yourself, how would you accomplish the same outcome. Often as you travel the thought process of an algorithm, you gain insight into the other developer’s implementation. Many of us have the inclination to re-write what we don’t understand. Resist this urge! The existing implementation is battle-tested and yours is not.

Some code is just vexing, reach out to a peer — a second set of eyes always helps. Walk the code together. You’ll be surprised what the two of you will find.

Here are 5 tips for leaving breadcrumbs for next developers

1. Patterns
Use known patterns, create your own patterns. Stick with a consistent paradigm throughout the code. For example, don’t have 3 approaches to data access.

2. Consistency
This is by far the most important aspect of coding. Nothing is more frustrating than finding inconsistent code. Consistency allows for assumptions. Each time a specific software pattern is encountered, it should be assumed it behaves similarly as other instances of the pattern.

Inconsistent code is a nightmare, imagine reading a book with every word meaning something different, including the same word in different places. You’d have to look up each word and expend large amounts of mental energy discovering the intent. It’s frustrating, tedious and painful. You’ll go crazy! Don’t do this to next developer.

3. Verbose Naming
This is your language. These are the words to your story. Weave them well.

This includes class names, method names, variable names, project names and property names.

Don’t:

if(monkey.HoursSinceLastMeal > 3)
{
    FeedMonkey();
}

Do:

int feedInterval = 3;

if(monkey.HoursSinceLastMeal > feedInterval)
{
    FeedMonkey();
}

The first example has 3 hard coded in the if statement. This code is syntactically correct, but the intent of the number 3 tells you nothing. Looking at the property it’s evaluated against, you can surmise that it’s really 3 hours. In reality we don’t know. We are making an assumption.

In the second example, we set 3 to a variable called ‘feedInterval’. The intent is clearly stated in the variable name. If it’s been 3 hours since the last meal, it’s time to feed the monkey. A side effect of setting the variable is we can now change the feed interval without changing the logic.

This is a contrived example, in a large piece of software this type of code is self documenting and will help the next developer understand the code.

4. Comments
Comments are a double edge sword. Too much commenting increases maintenance costs, not enough leaves developers unsure on how the code works. A general rule of thumb is to comment when the average developer will not understand the code. This happens when the assumptions are not obvious or the code is out of the ordinary.

5. Code Simple
In my professional opinion writing complex code is the biggest folly among developers.

Steve Jobs on simplicity:

Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.

Complexity comes in many forms, some of which include: future proofing, overly complex implementations, too much abstraction, large classes and large methods.

For more on writing clean simple code, see Uncle Bob’s book Clean Code and Max Kanat-Alexander’s Code Simplicity

Closing

Reading code is hard. With a few simple steps you can ensure the next developer will grok your code.

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