Reinvigorating a large Android code-base

The problem

We faced a classic problem on a recent project. Stop me if you've heard this one before. We are supporting a well established product with millions of users, running on a large underlying code base, but the code base has been building technical debt for years, slowly turning into a Frankenstein, causing support problems. The types of problems we were facing include:

• Separation of concerns violations making unit test maintenance and creation more challenging, with lower than ideal coverage
• Threading complexity in places leading to defects that are difficult to find and repair
• Numerous special-case customizations distributed throughout the code, each addressing specific customer needs that only activate for that customer
• Reduction in team velocity due to code complexity and the other issues listed above

Challenges of Addressing Technical Debt

In one of my first posts I touched on the challenges of balancing time spent paying back technical debt vs time spent adding new features. It is always difficult to find opportunities to address these types of concerns in a code base because the investment of a large refactoring effort is very high and the direct customer value can be perceived as low (especially outside of engineering) and difficult to quantify when compared to ongoing targeted feature enhancements and defect fixes.

I find the best approach is usually to address refactoring in an iterative approach rather than tackling an entire code base all at once. In this fashion you can balance new feature development with the need to keep your foundation stable.

Solution Overview

On this recent project, however, we were given a task to refresh the entire UI. This was an unusual opportunity to re-evaluate the code base holistically and introduce new concepts and techniques.

We targeted three primary technologies to aid in this effort:

• Dependency injection using Dagger2
• Decomposition of Android activities into Model/View/Controllers
• Usage of RxJava and retrolambda for component communication and thread management

Over the next several posts I will delve into the approach we took, how each contributed to solving the problems identified above, and some of the challenges we faced. 

Integration Test - Case Study

In the previous post I discussed an integration-test framework that I developed for testing system  interactions with a java  master controller. Recall that this system consists of separate View, Master Controller, and Model/ModelController components.

A basic flow through the system would start with an action in the View, pass through the Master Controller to the Model/Model Controller, pass back into the Master Controller, and then terminate with responses back to the view and the model controller.

For this case I configured Dagger2 to create a mock of the manager that interacts with the view (see mocked view in the diagram). I used the mock to verify that the correct method and method parameters are called at the end of the flow. I also wanted to verify that the correct methods and method parameters are called on the Model Controller, but I could not mock the Model Controller because it is integral to the flow we are testing. Instead, the test spied on the model controller to verify the expected method and method parameter calls.

Test flow (from diagram):

1. Initiate an action into the controller to mimic a user interaction from the GUI (View)
2. The Master Controller interprets the input action as an action needing interpretation and dispatch to the Model Controller and performs these actions. I used Mockito to verify that the correct Model Controller method was called and that the correct parameters were supplied.
3. The Model Controller interacts with the Model, processes the action and sends its own command back to the controller. I verify that the correct Master Controller method is called with the correct parameters. If the correct signature is seen, it means that the following components are working properly for the flow under test:
1. The JNI layer for passing between the Java Master Controller and the C Model Controller for these interactions
2. The Model Controller logic for this action 
4. The Master Controller then interprets the action from the Model Controller, dispatches an action to the View to inform it what to display, and replies to the Model Controller with a confirmation able to successfully interpret and handle the command. Both these terminal responses are also verified.

One added complexity with testing this integration is that there are several locations where the flow crosses thread boundaries. I needed a mechanism for delaying our verifies until a callback is received. The integration test will take much longer to run than a typical unit test (on the order of 15 seconds), but this does not mean that we can get sloppy and use sleep statements. To get reliable results, sleep statements would add more delay than necessary, and could still result in tests which are not repeatable. It’s almost never a good idea to sleep in an automated test...or in any code.

Mockito provides the doAnswer() method which is very helpful for mocking asynchronous responses from an object, but this is not helpful to us because we are testing objects which we can’t mock. What we need is a way of synchronizing with the asynchronous callback, because the code under test relies on that callback completing before it can proceed. Java provides such a mechanism with the CountdownLatch. The CountdownLatch will block until its counter reaches 0. We can setup the CountdownLatch in the test thread and give it a value of 1. In the callback thread, we reduce the count to 0 to unblock the test thread when we perform the callback, allowing it to proceed with its verification. We attach a timeout to the latch in case the callback is never hit (15 seconds in this case). This is much better than a sleep statement because the timeout condition is only hit on a failure condition. For this reason we can choose a large number that we know will pass under all conditions, without actually delaying the test execution time except for the exceptional condition of an actual failing test. 



This solution is still not ideal. We had to instrument the test with a handler that overrides the implementation we are testing to call the implementation’s body and then countdown the latch. I could have used DI to inject this test handler, but in this case the framework already had a mechanism for registering handlers directly, so we were able to register the test handler this way.

Developing an Integration Test Framework - Case Study

In the last two posts I described a test strategy and unit tests for a controller written mostly in java. The Master Controller interfaces with an http server hosting the View and a Model/Model Controller written in C. This Model/Model Controller was developed by a different software group and is like a black box for our purposes. I wanted the capability to test both our C/JNI layer and also test drops of the Model/Model Controller from the other software team. Integration tests provided me with the capability to accomplish both these goals, while also testing interactions between the modules unit-tested in isolation. I could test these interactions from the Master Controller with java test frameworks by observing expected system responses to stimuli, removing the need for an entirely separate C-based test framework. 

The Master Controller code splits its core functionality between "managers" with specific responsibilities, including handling boundary crossings to the View and to the Model/Model Controller. Each manager is housed in a container class. This is an excellent spot to introduce dependency injection for test components, as shown in the ManagerControllerSample.java below: 

I created a test framework that allowed for each integration test to determine for each manager whether it would be implemented as a mock, using the standard implementation, or using a spied version of the standard implementation. With Dagger2, I was able to inject the appropriate component (test or production) at run-time. In Graph.java we see the dagger graph. IntegrationTestBase.java  specifies the list of managers to mock and spy as seen in the call to initGraph(), and ManagerTestDataModule injects the appropriate manager type (standard, mock, or spy):


In the next post I will discuss an integration test that used this framework and a technique for synchronizing callbacks spanning thread boundaries.

Creating an Automated Test Strategy - Use Case

Background

On a recent project I was working on a team developing a software component as part of a larger system. This component can be thought of as a controller in a distributed MVC system, if you think of the model as having its own embedded controller. Our controller (the Master Controller) was written in Java. We had a JNI layer for interfacing with the C-based Model/Model Controller assembly and communicated with a web server hosting the view. I wanted to develop an automated test strategy that would:

• Validate the Master Controller
• Guard against regressions in in the Master Controller
• Validate system interactions
• Allow us to develop functionality independent from schedules/delivery of new functionality in the View and Model/Model Controller components

While most of this could be accomplished with unit tests, I decided we also needed some level of integration test for validating the system interactions. In the following description, bear in mind that the Master Controller is the device under test (DUT) and it interfaces with the View and Model/Model Controller but does not test them directly.

Strategy

It is always a good idea to discuss and document your test strategy. It was even more important for this project, as the other developers on the team were not as familiar with standard automated test tenets, coming from environments where automated test was not a priority. I went about this by performing the following steps:

• Develop and document the proposed strategy on internal project wiki
• Gain team buyin
• Implement integration test framework
• Create tests exemplifying usage for a variety of different types of modules
• Provide training

Our overall strategy was to rely on unit tests for the majority of functionality test, but provide a powerful integration test environment and a small number of tests validating interactions between these components.

Unit Test

There are some widely adopted principles for creating unit tests. For the wiki and the training, I put together a few simple patterns/anti-patterns. If you are at all familiar with unit test, there will be no surprises here:

Do

• Test each module in isolation
• Test edge conditions
• bad or null inputs
• etc
• Keep tests fast
• Ideally milliseconds
• Keep cyclomatic complexity low

Don't

• Allow timing dependencies other than timeouts
• No sleeps/timers
• Span threads in one test
• Depend on other tests or order of execution
• Leave artifacts
• Use @After methods (jUnit) to ensure that artifact cleanup will happen independent of test failure

I chose jUnit, Mockito, and PowerMock as the tools for our unit test. This decision was based on familiarity with the tools, their popularity, and suitability for usage within our system. Mockito performs most of the mocking functionality we needed. Mockito allowed us to:

• Handle external dependencies easily
• Good unit tests only test the module under test, none of its dependencies
• Mock responses from these dependencies
• Verify the method calls on these dependencies, including
• Parameters passed
• With stock matching algorithms or custom validators
• Number of invocations
• Verify that method that shouldn't be called are not called
• Mimic asynchronous callbacks from mocked objects

PowerMock provided us with the key additional capability to mock static method calls.

Integration Test

Although unit testing covers the bulk of the test for the product, it is also useful to validate interactions between the components. In the next post I will describe the integration test strategy I implemented for this project in detail.

System Test

The unit and integration tests created and supported by the development team were only one piece of the overall validation strategy. The QA team tested the overall product manually and with Selenium for creating automated tests driven from the html5/javascript View component.

CI

For tests to be effective, of course, they need to actually be run frequently. Ideally developers would all run the unit test suite before checking in, but this is not enforceable. We had a Jenkins CI environment, where we setup automated tests running on a fixed interval whenever code changes were checked in. Failures were reported and logged and emailed to the team for resolution. We also tied in a code coverage tool for reporting progress against our goals.

TDD

For those who are not familiar with test-driven development, the basic idea is that you create your tests before you implement your code using a recipe like:

1. Create your interfaces
2. Create your tests to these interfaces
3. Implement your code
4. Run your tests
5. Rinse/repeat as necessary until all tests are green (pass)

Some of the benefits of TDD are:

1. Enforces up-front accurate requirements and up-front interface design
2. Improves code readability, interface design, architecture, quality (clearly much less likely to make untestable code :))
3. Ensures that tests don't fall behind implementation

As part of this project I adopted a TDD approach, although found it difficult to adopt whole-heartedly. I found "concurrent" test/development a better fit rather than strict adherance to the recipe. It definitely took longer to take a TDD approach than it would have to simply perform the code, but no longer than it would have to develop the code and then the tests later. I advocated for similar approaches by other team members as part of the team training, but we did not enforce it.

Up Next

As already mentioned, my next post will delve into the integration test strategy and methodology that I adopted for the team on this project.

Advocating for Robust Automated Test

The conundrum

In virtually all software engineering organizations I have worked, management is aware of the benefits and importance of a comprehensive automated software test suite and the pitfalls of scrimping on test. Yet, it's actual application varies widely across projects and organizations. Often I find that automated test is adopted to some extent, but with insufficient rigor, thought, and attention. Occasionally I have seen teams that don't do automated test at all. In almost every team I have worked, when there is a crunch to get something done, testing rigor is relaxed, dropped outright, or at least postponed until after the crunch is over.

Clearly there are pain points in the process which is leading to this situation.

Pain Points

• External schedule constraints
• • Deadlines, deadlines, deadlines
  • Customer demo tomorrow
  • Emergency of the day
• 
  

  Schedule constraints are often be the biggest reason for reduced rigor in testing methodology. It is especially difficult when they are immutable deadlines coming from external sources such as: 

  
  

  • Software delivery to a hardware devices or systems whose release schedules are wholly dictated by hardware availability
  • • For example, when working on pre-installed app for a phone, the apps usually either make the delivery date or are pulled
  • Trade shows
• Challenge of quantifying the cost-benefit trade-off
• How do we know that we aren't spending more than we are getting in return?
• Test maintenance burden
• Difficulty of retrofitting tests into a legacy code base 
• Up-front cost
• • Test Framework identification
  • Developer training
  • Developer mindset shift
  • CI integration
  • Difficulty in getting "valuable" test metrics

Addressing the Pain

• External schedule constraints

One strategy to deal with a situation where there is insufficient remaining time to both deliver the software and a full set of developer automated tests is to:

1. Identify all the required tests and add them to the backlog
2. Deliver as many of the most critical tests as possible
3. Supplement the reduced set of automated tests with a more extensive QA test (both manual and automated) for the immediate deliverable
4. Schedule the remaining unfinished tests for delivery in the following sprint, as soon as the “fire drill” is over

In some especially reactive environments it can be challenging to break out of this “fire drill” mode, as they cascade upon each other. This could be a sign of issues that need to be addressed at the management level,including:

1. Unwillingness to turn away new business
2. Unrealistic expectations
3. Lack of understanding of the impact of these decisions
4. Insufficient development resources

It is a responsibility of the senior members of a developer team to point this out to management and help work out a plan to remediate the issue. Depending on team charter and business conditions this could be a very difficult problem to solve and isn’t necessarily a management failure. Developers should take an active role in sharing the responsibility associated with improving the process and steering the process back to sanity.

Even in the most aggressive environments, there will always be “some” down-time, which could be dedicated to catching up on test. This is a good time to advocate for sprints focusing on automated test get-well.

• Quantifying the cost-benefit trade-off

     Perhaps the best way to get upper-management support for test is to demonstrate that the cost in delivery schedule and engineering resource consumption is outweighed by the benefit. This can be challenging to quantify, to say the least. Metrics such as:

1. Severity and frequency of bug reports
2. Savings in support development time
3. Savings in refactoring time
4. Increased revenue due to release of a higher-quality product
5. Less need for refactoring 

are difficult enough to measure when you have the data. But, of course you can’t have internal data until you have a well-tested code-base to compare against, so it’s a bit of a chicken and the egg. One approach would be to present these benefits qualitatively, not quantitatively and reference cost-benefit tradeoffs published by other companies who have made this investment.

There is a severe penalty for “catching a code-base up” so the benefit is greater when applied at the beginning of a project.

• Test maintenance burden

     There is clearly overhead associated with maintaining tests. The amount of this overhead can vary dramatically on factors such as:

1. Proper test scoping
2. Test repeatability
3. Test complexity and supportability

When the tests are properly scoped, repeatable, and straightforward maintenance cost is not so great. Systemic violation of one or more of these factors can easily push the maintenance cost so high that the cost exceeds the benefit. Sometimes a developer or manager will have had past experience working in environments that have embraced automated test, but improperly applied some of these constraints. This can lead to a belief that automated test “is not worth it”. It can be very difficult to challenge a belief system when it is based on experience! It could help, when encountering someone who doesn’t believe in automated developer test to push into the experiences they had, understand where it may have failed, and explain how it might have worked better if applied differently.

• Difficulty of retrofitting into a legacy code base 

The value of adding good test coverage to a legacy code base is less and the cost greater than if it were applied from the beginning of the project. However, in such environments it may still be valuable when applied judiciously and iteratively in small chunks. For example, before refactoring a bit of buggy, complex, and/or obdurate code, it is helpful to provide strong test coverage for the methods in question and use this to validate the refactored code. Similarly, any time that new functionality is added, good test coverage for that new functionality can be easily justified. Finally, there is always “some” down time in a project, which can be used to bolster tests. I like to target areas of the code that are:

1. Most problematic (highest bug reports)
2. Functionally critical
3. Core (used by many components)
4. Most complex
5. Most likely to change

• Up-front cost

While there are initial costs associated with identifying and implementing a test framework, integrating with CI, and training developers, these costs are manageable and largely scale across multiple projects.

Next up

In upcoming posts I will discuss an automated test strategy that I advocated and adopted for a recent project.

The Importance of Design, Architecture, and Clean Code in a Startup Environment

I've worked for several startups, and practically all suffer from the same basic problem: money. It is a race against time to develop enough customers and/or revenue before investor interest, and thus money, disappears. But to get customers, you need code...usually lots of it. So, naturally the focus is on pumping it out as rapidly as possible. The catch-22 is that practically as soon as the company starts to turn the corner and become profitable that code can turn into a liability. Often companies pass the critical early phases, only to fail at scale because of short-sighted decisions in getting to that first corner.

For emphasis, I will give one extreme example I encountered at a very small startup many years ago where I was managing engineering. I inherited a hastily written code base as a starting point (developed by people who were engineers, but not software engineers). The lack of focus and interest in good code and development practices is perhaps best exemplified by the words of one of the co-founders that I will never forget. In a status meeting attended by the development team I  suggested that a developer at least extract a chunk of code that was "cut-and-paste" repeated into a single method. The Co-founder was at this meeting and interjected that this should not be done "if it might slowdown" the developer. This input, and even the notion that extracting code into a method would slow someone down, was a real eye opener. This was the most extreme case I've seen, and largely influenced by the fact that both the developer and the co-founder were hardware engineers, not software engineers, by background,  but the point is that sometimes even the most basic design and supportability tenets are disregarded in the flawed assumption that this is somehow justified in a startup environment.

In this case (as with most startups) the company did not survive long enough to become profitable, but what if it did? Would the weight of the technical debt sink the company? How could such a product be supported? Would there be the time and resources available to completely rewrite the code or would the company simply get bogged down in never-ending feature enhancements on a foundation that was already crumbling under its own weight?

So while some would argue that good practices is a luxury in startup environments, I would argue that the cost of completely disregarding good practices altogether assures failure. Clearly time is of concern, but "well-written" code need not necessarily take significantly more time than a complete hack. Some basic coding concepts can be applied with little or no additional time, including:

• DRY (Don't Repeat Yourself )
• Self-documenting code
• Using the composed method pattern
• Choosing well named methods
• SOLID principles

Beyond basic coding principles, it is equally important to spend time to design and architect the solution and at least consider the evolution of the system. I'm a firm believer that design can be broken down into iterations, similar to other coding activities. Start with the basic architecture required for the first milestone and map out where you expect it will need to evolve from there, expecting that the future iterations will change as requirements change. I'm not advocating for gold-plated designs, but you need to understand some basics of where the code will evolve to avoid coding into corners. I think you should fully understand the architecture requirements of the first iteration, and perhaps the next. For iterations beyond that, at least devoting some time to think about how well the existing architecture will scale can avoid pitfalls today that will be very costly tomorrow. I think this work should always be done in collaboration with other senior members and stakeholders of the team and should be supplemented with some form of "light" documentation. A day or two of due diligence can pay off dramatically over the lifetime of the project.

Delivering Highly Configurable Libraries Using Dependency Injection

In the last post, I described usage of a factory pattern to provide highly configurable functionality. An alternate approach would be the usage of dependency injection(DI). At the time I designed these libraries, dependency injection platforms on Android were not as mature as they are today. Guice was starting to gain some popularity, but uses reflection and can impact runtime performance. With the advent of Dagger2, we have access to a static dependency injection solution that does not rely on run-time binding. 

Using Dagger2, I could replace the previous abstract factory pattern. I decided to explore this solution as an exercise. Note that I did not actually implement and compile this solution and humans are notoriously lousy compilers, so it's possible (likely) there are errors. However, it should illustrate the general approach.  This also assumes that you are generally familiar with usage of Dagger2.

As commented in the code. To override this behavior, the user could provide their own graph and data module and call ApplicationDispatchHandlerDaggerSample.registerHandlers() at runtime.

While some might argue that the DI solution with Dagger2 is cleaner, it would impose an additional constraint on each user of the NAC API to download and learn its usage. This is a relatively minor cost, but you could make the argument that in the interest of keeping NAC as easy to use as possible, the Dagger2 approach would add complexity with no real end-user benefit. A bigger issue with this approach is that, since we don't know which modules the user will want to  inject a-priori, we inject them all. This means that a user would have to specify handlers from within the NAC library, which  the user should not even need to know about, and which we have been keeping private and obfuscated. There are probably ways we could fix this by exposing methods to get the default implementation for a class, but this seems to just add more unintentional complexity. My conclusion is that, while DI is an important and highly useful pattern, for  this  specific use-case, the pattern we chose was an overall better approach.

Enumerated Types - Java

Coming from a long C++ background, one of my favorite constructs in Java was the simple enumerated type. I found the added full-fledged, "class-like" behavioral characteristics useful from the beginning. Just the simple ability to specify a string value in the element constructors alone almost doubles the power of a C++-like enum because it allows you to essentially make String enumerated types when coupled with a toString() method overload. Of course there is much more power to the Java enumerated type's class-like capabilities than just this. One of the first really interesting usages I found for an enum was to create a simple state machine. I no longer have the source code, but if you are interested in how to do this web search for it, there are others who have done this.

When creating API's I find the bounded nature of enums to be self-documenting when used as parameters such as keys in key-value pairs, as opposed to traditional usage of strings. Additionally it removes the possibility of bugs from a consumer of the API making a typo in a string key. However, what if you are releasing an API that has both a defined set of key values and the possibility for unknown key values. I find that sometimes you have the need for an "extensible" enumerated type. For example, in one case we were delivering an Android library for usage in client software with a server-side back-end component that could add functionality over time. I wanted to provide the capability for a customer to use new keys delivered via new server functionality even though the library itself was unaware of these keys at the time of the library creation. However, I still wanted the self-documenting, less error-prone way of specifying keys provided by an enumerated type. To get both behaviors, we introduced a simple construct that we called DynamicEnum. A DynamicEnum is a class, not a Java enum, but uses String constants to enumerate the known values.

The DynamicEnum thus has the benefit of providing a set of values for all known element types, and can also be extended by adding additional values. In an upcoming post on usage of the abstract factory pattern I will describe why this can be useful through a real-world example.

Use Case: Being a Code Custodian, Manual Inspections

This is the third and final post of this series. Please see the last two posts for my thoughts on what it means to be a code custodian and the application of automated inspection tools in this role.

While automated tests have a place in detecting potential bugs or maintenance issues for a code-base, I find that only manual inspection can detect issues in proper usage of the classes, methods, and design patterns in use for a particular code base. At a recent positions, some of the stylistic things that I looked for in a manual inspection relate to reducing accidental complexity, and include:

• Crisp and clear interfaces, especially customer-facing API's
• Well-documented API calls
• In this case, we used Javadoc
• Many of the developers were non-native English speakers, making this more critical for our team
•  Well-named methods
• Proper usage of existing design patterns within the code base
• Avoidance of internal packages
• In our case, we had libraries that had external and internal packages. Sometimes developers would accidentally reach down into an internal package of another library
• A better approach would have been to find an automated approach for verifying compliance
• Proper application of standard object-oriented practices
• Stylistic consistency
• At a gross-level, without being intrusive

Of course, I also checked for more substantive issues like potential threading issues and other defects, but often the stylistic issues are more noticeable.

To avoid getting behind, I would review checkins on a daily basis on average. With about 10 active developers on the team, it wasn't practical to review every line of code that was checked in. To limit the time commitment I applied a few filters to guide my engagement level:

• Individual developer expertise
• Does this a developer have a track record of committing high-quality software?
• Does this developer have experience in the area of the code that is being changed?
• Type of code under change
• Is this a core or performance-sensitive area of the code, requiring more attention?
• Are the modules under change complex and/or already in need of refactoring
• Customer-facing
• Is this a customer-facing API or an internal method that will be obfuscated away?

We had a policy whereby developers were encouraged to ask for reviews of changes to critical and/or complex sections of code. The best developers in the group used this policy judiciously, and I would seldom review checkins by them unless they requested a review.  On the flip-side, if a developer had a track record of sloppy checkins and/or was working in an area of the code with which s/he had little familiarity I was likely to take more notice.

Having a role of technical lead, does not make anyone the expert on everything. I interpret the code custodian role to mean that I should coordinate with other experts on the team to help identify areas of risk and coordinate collaboration, not try to be the sole authority on everything. It's always helpful to get another set of eyes on your code, and I would frequently solicit code reviews for trickier areas of my own code submissions from one or two other senior members of the team. Additionally, I would sometimes ask for help from these same members in performing code reviews of submissions from others on the team that I find to be in a critical portion of the code that I have less familiarity with.

Of course it is very helpful to be intimately familiar with the software before taking on these types of reviews. In this case I had designed and coded much of the initial code base, which made the job easier. Were I to drop into a new code base, I could still look for general code and styling issues, but would be less apt to give any valuable feedback relevant to fitting into existing design patterns and proper usage of libraries, etc.

In an effort to preserve developer freedom, if the issues I detected were all small or stylistic, often I wouldn't say anything, but rather lock it away in case the advice might come in handy in the future as part of more substantive feedback. If the issue is in naming of methods and/or javadoc and the user is not a native-English speaker I might make the change directly in the code base and check it in since the changes are non-controversial and straightforward. For most other types of feedback I would usually make suggestions in an email. Developer woud either follow the suggestions or provide good reasons for not following them. Lastly, I find it can be helpful to reference well-known books by experts in the field when making a point to give it more credence and make it less of a single person's opinion.

In this series of posts I discussed the role of a code custodian in maintaining a clean base. I stress that this is not always an appropriate role and there are many different approaches and techniques. I don't necessarily advocate for the approaches laid out here, but list them by way of an example that worked well in one environment. I found the manual and automated inspections to be helpful but perhaps they would be too intrusive or unnecessary in another environment. In a third environment, perhaps more stringent techniques would be appropriate.

Use Case: Being a Code Custodian, Automated Inspections

As stated in my last post, the roles of an architect and/or team lead can vary dramatically depending on team dynamics. Being a code custodian may or may not be appropriate for your organization. On the one hand, it's hard to argue against the importance of having a cohesive code base with similar coding patterns and standards for quality. On the other hand, a heavy-handed environment can be very disabling for developers and reduce team members' feelings of empowerment. So how best to balance these competing factors?

A recent long-running project I worked on had the following team dynamics:

• Varied developer skill levels
• Fresh from university to senior
• Combination of remote and local developers
• Most remote
• English skills that also varied in quality between team members
• Mix of time working in the code base

Although we had some written coding guidelines, I find this to be a largely ineffective way of managing a code-base. We did have some standards, but the best standards are already available and published in books such as "Effective Java" by Joshua Bloch and "Clean Code" by Robert Martin.

Instead, I chose to rely on a mix of automated and manual inspection approaches. See my last post for a comparison between these approaches.

Perhaps the most important tenet to developing any of these strategies is to get active participation from other members of the team in their usage and application. Some of the inspections by these tools can feel intrusive and even arbitrary at times. Getting the team or a subset of senior developers on the team to buy-in to the list of checks is important to foster a feeling of ownership to the policy and to avoid tying developers down and hampering creativity. it might take a couple of iterations to refine the rules.

For this situation, we used CheckStyle, FindBugs, and lint in out suite of tools that were run on Jenkins. Checking stylistic rules can be particularly treacherous if applied too heavy-handedly. When looking at CheckStyle, the first thing I did was to immediately drop silly rules such as where semicolons or spaces are placed. There is nothing worse than breaking the build because you missed a spacing constraint. I think that it's important to maintain consistency in the code-base, especially within an individual module. But this consistency does not need to be enforced via an automated tool.

However, that doesn't make the likes of CheckStyle worthless. There are a number of checks within CheckStyle that, when enforced, aid in keeping the complexity down in the code base. We used a number of CheckStyle rules, but the following were somewhat helpful in keeping code complexity manageable:

• FileLength
• MethodLength
• ParameterNumber
• AnonInnerLength
• AvoidNestedBlocks
• MagicNumber
• Nested* (various Nested rules)

Some of the other methods, like HiddenField, were also useful in avoiding bugs.

FindBugs and lint (and occasionally CheckStyle) were good at identifying potential defects and leaks. At first the tools detected a number of issues, but over time there were fewer and fewer violations.

Having good reporting mechanisms was very important. At first our reporting mechanisms were very poor, requiring a lot of digging to find the source of the build error. This was largely due to the limitations of the central build CI system we were using. When we shifted the tests to run on Jenkins, the reporting was much crisper, making for a much better experience.

An Extensible Factory Pattern - Use Case

NuanceAndroidCore (NAC) was designed to be a highly customizable library, facilitating creation of Siri-like voice assistants, without imposing constraints on the look and feel of these assistants. The platform controls the voice dialog and provides callbacks to the UI layer for displaying recognition results and status of the recording and recognition. Consumers of the library render this feedback as they see fit.

The library also performs data lookups such as calendar events and alarms within a range of dates; and actions such as launching an application, adding/deleting/changing alarms, events, and playing music. Much of the benefit of the library comes from its ability to perform all this seamlessly and automatically. However, in many cases, individual customers might have their own implementations for one or more of these applications, with their own API's, and sometimes with added functionality. The challenge was to come up with the best way to support these customizations, control and manage the intricacies of the voice dialog, and supply default behaviors for everything else.

This is where the extensible factory proved to be useful. NAC used this pattern in several places, including creation of its action and lookup handlers. NAC registered its own implementations of each handler (for example a handler for sending an SMS message). Each customer could then customize specific actions by registering a different factory to override the default implementation, should they desire different behavior. In this manner, they could leverage everything else the library provides, including everything related to the voice dialog for that action, and any actions that didn't need customizations, and only provide code for specific overrides of the default behavior.

Each of the action handler factories are chosen from a map keyed off an element of the server response (for example, an action of type "sms" would key an action handler of type SMS). Building off the sample code introduced in the last post about dynamic enums, the factory method looks like:

(Disclaimer: This snippet was hand-edited from the original code and not compiled. The possibility exists for compilation errors.) 

This technique relies on reflection to build up the handlers, but performance is not a concern because the construction of the handlers is a very small portion of the overall time to perform an action, typically dominated by the time in getting recognition results returned from the server.

The voice recognition logic  and dialog management for this system is server-side.

So the opportunity exists for new voice domains to be added and deployed to the field that NAC is not configured for, after NAC has already been released. This is where the dynamic enum class discussed in the last post became useful. With the dynamic enum, NAC could register all its known handlers with immutable keys, while also supporting key types it doesn't know about. For example, the SMS key type would be defined and used by a customer wishing to override default SMS handling, but the customer could create a new key, say "sports" on the fly should the "sports" domain be added to the server domains after the NAC library has already been released.

In the next post, I will explore an alternate approach using Dagger2 for dependency injection.

Being a Code Custodian

The roles of a team lead can vary dramatically depending on the team dynamics. Being a code custodian may or may not be appropriate for your organization. I have found it valuable in some situations. There are automated and manual approaches to monitoring the integrity of software. I like to apply both when appropriate. Each has pros and cons:

• Automated Approach - Usage of software tools to detect potential defects and/or style violations
• Advantages
• Can check for a large number of potential defects and/or design smells
• No ongoing time investment after initial investment to setup and configure
• Can be tied into CI environments like Jenkins to guarantee adherance
• Disadvantages
• Not good at catching the most important issues
• If rules are configured too restrictively, becomes more of a hindrance than a benefit
• Manual Approach - Human review of checkins or some subset of checkins
• Advantages
• Only way to catch many types of issues
• Keeps the reviewer abreast of new development
• Leads to mentoring opportunities for the reviewer
• Good way for the reviewer to learn from other approaches and pickup new techniques
• Disadvantages
• Difficult and important to be completely objective and remove opinion from feedback
• If applied heavy-handedly 
• Kills developer empowerment
• Can be disastrous to morale
• Time-intensive
• Spotty
• Usually unrealistic to fully check everything

I would further decompose automated approaches into 2 categories.

• Category 1: Surface Analysis - Static-analysis tools including lint, checkstyle, and findbugs
• Advantages
• Customizable
• Rules-based
• File-driven
• Easily integrated into many modern IDE's
• Easily integrated into build or CI environments via a CLI
• Many free or open-source
• Often mature and robust
• Disadvantage
• While they can catch many "surface" issues, they don't always dig very deeply
• Category 2: Deep Analysis - Tools that looks more deeply into aspects such as cyclomatic complexity, memory usage, and performance. 
• Advantage
• Tend to be higher value 
• Disadvantages
• Difficult to automate
• Some intended more as an aid for manual test and debug
• Some produce results that should be inspected and interpreted
• Can be difficult or impossible to quantify rules for
• Less typically free or open-source

In the next two posts I will delve into how I applied these tools in recent projects.

Design and Architecture in an Agile Environment

I have worked in several "scrum-like" agile environments over the years. I say scrum-like because each adopted some of the principles, but was unable to embrace all. In almost every case the biggest casualty tended to be an inability to totally isolate the development team from shifting priorities in the middle of a scrum. This is a difficult problem to address, particularly when working with exceptionally demanding customers, and perhaps a topic for another day. Another possible discussion item is whether or not "partial-scrum" is scrum at all.

One of the industry trends with agile environments is to tradeoff up-front design and architecture for an "emergent design" iterative environment where coding starts immediately. However, I would argue that agile does not, and should not, imply that there is no up-front design. Iterative development does not mean that you should rewrite the entire system in every iteration. Nor could anyone reasonably argue that this is an efficient use of the team's time. Yet, if you jump into implementation without any design the chances of "coding into a corner" greatly increases, potentially producing exactly this type of situation.

So we shouldn't give up design and up-front thought any more than we should give up good coding standards. Fast development does not have to equal reckless development. A technique I have used in some organizations is to have one or two senior developers/architects sign up for design and architectural tasks as deliverables for the sprint. These deliverables can be tracked much like any other deliverable, and are as integral to the sprint's success as code and QA deliverables. The end-result should be a light design in some electronic format, with buyin from the team and stakeholders.

The only real disconnect with the scrum process of adopting this technique is that typically the one-or-two people working on this task have a different focus from the rest of the team. This work is synergistic, however, as it results in a better definition of development backlog tasks for subsequent sprints. From this perspective, the team performing these tasks could be their own scrum, but often this is not practical because of how small the team is (could be one member) and because the members will often also take other tasks from the ongoing development sprint.

Another approach that I have seen work is to break up chains of sprints with planned one-or-two week intervals for the team to do design and architecture of  backlog items and cleanup work from prior sprints as needed. Whether this work is considered a sprint or not is up to the team, but I have found that occasional "unstructured" time is also useful for a team.

"Does design and architecture play a role in scrum? If so, what techniques have you seen work and which have you seen not work?"

Balancing Performance with Code Readability : Case Study (Android, Java)

Google has a number of blog posts on how to write code for Android performance. They make recommendations that conflict with normal Java standards such as avoiding enumerated types and using counter-type  loops over their collection types. They make good arguments for comparative performance improvements for each, but I think it's good to step back and evaluate Google's motivations before blindly applying all their recommendations.

• Motivation 1: Bad apps make Android look bad
  
  From Google's perspective, a few misbehaving apps can make the entire ecosystem look bad when compared to the highly controlled ecosystem of Apple. This is especially true for apps that are bad citizens (e.g.- consume more resources than needed for longer than needed, services that run longer than needed, activation of power-hungry resources like GPS hardware for longer than needed). Other recommendations are performance-oriented. There is little motivation for them to recommend well-written code over code that grabs every nanosecond of performance. They aren't the ones supporting the apps, after all.

• Motivation 2: Wide range of hardware capabilities
  
  Often times developers will test only on a high-speed device over WiFi or 4G and not understand the implications of how their app will run on older or less expensive hardware or over slow Edge networks in China or elsewhere. With the advent of smart devices like watches, the limitations can be even more severe, especially for  battery life.

While I strongly endorse writing programs that are good citizens, other recommendations to grab every nanosecond of performance I often find as not always necessary. I also have the luxury of architecting, designing, and developing apps for specific high-end Android devices, which makes motivation 2 less relevant. Even if this were not the case, I still prefer to write first for readability and, only when proven necessary, refactor for performance.

An example of where I recently found this  useful was in creating code whose function is coordinating an external set of contacts with the Android native contact database. There were a number of fields that were to be synchronized and the list of exactly which would be synchronized was subject to change. I found code in another application that someone wrote to do something similar as a reference. The author of this code created a string projection  where each ordinal was referenced by number into that projection. Insertions or deletions of values in this projection would change the meaning of each ordinal, making the code very brittle.

I introduced an enumerated type for storing the elements of the Android database string projection and relied on the ordinal position for later retrieving columns from this projection. The structure was thus "self-aligned" making for easy removal and addition of elements to the projection. The enum looked something like this:

Access to the columns, could then use the ordinal of the enumerated values like so:

instead of using "magic numbers" which rely on the order of the elements in the projection (in this case it would have been "1").

The new self-aligned approach is both much more readable and less susceptible to bugs as the code changes and there was no noticeable performance hit.

Balancing Performance with Code Readability

This is a topic much discussed in the industry and the position I take is a common one: Code first for readability and support, refactor for performance as necessary. There are notable exceptions to the wisdom of taking a readability-first approach. In general you will know if your code is one of these exceptions. It could be graphics-intensive operations, operations widely used and distributed in libraries, and similar situations where you know critical paths through the code a priori. For the 90% case, where squeaking out every nanosecond of performance is not critical I like to use the following steps:

1. Write code to be as readable and supportable as possible
2. Validate that the performance is acceptable
3. If performance is not acceptable, profile to find the hot spots
4. Identify techniques for improving performance of these hot spots
5. Using the Strategy Pattern
1. Extract each module to  be refactored into a separate component and introduce a simple factory to serve up the existing implementation
2. Introduce a higher performing implementation and update the factory to use this new strategy
3. Add comments as necessary to the new strategy implementation

The factory gives us much more flexibility. It allows us to restore the original strategy if we later find that we really don't gain enough overall performance to warrant the new version. It also allows us to experiment with different strategies for improving the performance before deciding on one.

I am of the school of thought that a lot of comments in code is a design smell that the code wasn't as well thought out and decomposed as it should be. I think that most code should be written to be self-documenting with appropriately named methods with small scope. There are certain exceptions to this. Writing for performance is a common way of adding unintentional complexity. Often this added complexity is unavoidable. We should add sufficient comments to explain the code and the reasoning behind the extra complexity so that someone further down the road does not reverse our decisions (perhaps for readability) without understanding the implications of that reversal.

Next week I will push into a use-case where I opted for readability over performance in conflict with published performance recommendations from the platform vendor.

Effective Code Reviews: Case Study

As mentioned in previous posts, mentoring is an important and rewarding aspect to software leadership. One of the more effective ways to teach is via thought-out code review feedback. Performing code reviews is also an excellent way for the reviewer to learn and pick up new tricks. I often find it helpful to be on each end of the code review.

I solicit code reviews of my work when coding something particularly mission-critical and/or complex and encourage the same from other members of the team. There are many ways to run code reviews and perhaps I will discuss this in a future topic. Code reviews can involve a team or be one-on-one. The case study discussed here was a one-on-one case where I was the reviewer. Usually this has worked well for me. This situation had some unique challenges, however. Specifics of this situation:

• Working with a senior, talented, open-minded developer
• Developer is a non-native English speaker
• Developer is very open to feedback
• Developer's style resulted in overly complex code
• Developer frequently requested reviews
• Most of the request reviews span numerous modules

Problems this posed for me:

• Often times the code was just too complex for me to perform an effective review and root out potential side effects
• The language barrier reflected itself in the code in the naming of methods and variables
• The time demands of the frequent requests were too high

To address the time constraints, I applied a common technique, which was to ask to limit the scope to areas of highest concern. This did not work, as the developer was not able to identify specific areas. The inability to identify "chunks" that could be easily identified to treat in isolation was another symptom of the complexity.

This situation continued over a period of weeks. The bulk of my feedback centered on addressing the complexity issues and was in the form of email. However, each time I did this I felt I did not grasp the nuances of the changes well enough to provide adequate feedback. I found that the developer would make the specific requested changes and then the next day make the same complexity mistakes in the next piece of code that he submitted. To make matters worse, we were in a critical part of the project where time pressures were significant.

The real fix is to fully refactor the functionality. This is something we scheduled, however, it was not realistic in the short-term, so it was important to stabilize the code as much as possible. Equally important was my desire to help this talented developer with his complexity issues so that the problems would not be repeated in future projects.

Finally I decided that, despite the time-zone challenges and the additional time demands, the most effective way to perform reviews in this situation was to do them interactively with the developer. While voice is normally my first choice, in this situation I found that interactive IM sessions were more effective. There are two reasons for this:

1. It gave both parties an opportunity to think carefully before each reply
• From the developer's side this extra time gave him the opportunity to translate as necessary. 
• From my side it gave me time to analyze and provide recommendations as we went through the code.
2. It provided a written transcription for reference during and after the discussion. 

My biggest hurdles were understanding the motivations and side effects of each of the changes. So the review then became something like this:

• Me: Method X, lines Y-Z: What are you doing here and why?
• Dev: Explanation
• Me: Did you consider factors A and B?
• Dev: Actually, I think there may be a bug C 

( One of the things I like best about code reviews, whether I'm reviewing or being reviewed is that in discussing the code often the coder himself will stumble on issues)

• Me: What if you took this 4-line return statement and simplified it to a method call

etc.

The end result was that I felt I was finally able to provide effective feedback and identify specifics of how to reduce the complexity of the code.

The lesson learned here for me is to not wait so long to consider alternate communication mechanisms if I find one mechanism is not working well. This is a lesson I try to apply to many other aspects of communications.