While mobile vendor giants struggle to produce the most intellectual device, it appears that a huge amount of users want to buy a device as cheap as possible but still prefer smartphones over old-school feature-phones. This trend is mostly driven by a huge demand from emerging markets such as China and other Asian countries. Sub-$100 Android devices become a significant part of the market. There are rumors that the next version of Android will be aimed at low-end phones market, which means that this trend will continue to emerge.
This trend is leading to a great opportunity for app developers. However, with every great change come new challenges. Low cost devices usually have limited memory, small screen, slow CPUs and other limitations. App developers should care about performance of their code like never before.
There are three basic rules of writing applications with a great performance:
- Write efficient code.
- If the code is not efficient – profile and fix it.
- Continuously ensure that code is still efficient (ideally, after every single commit to your source control system).
While items 1 and 2 are obvious, item 3 requires additional explanation. Our experience shows that most software developers start to care about performance of their app after users already posted angry comments on Google Play. It comes even harder to track app performance metrics when your app is big and complex and you ship new versions frequently. The problem is that it is much easier to fix performance issue right after it was introduced than in case it is here for a few weeks and tons of code dependent on that issue were written. Of course manual regression testing is inefficient in this case – you simply can’t do it frequently enough. Fortunately, there are things like continuous integration that make it possible to automate this routine and thus detect and fix performance issues as early as possible.
In this document we are going to focus on items 2 and 3, as the art of writing efficient code is bit complicated for a single publication.
Performance tuning of every Android application differs from application to application. However there is a general flow. It looks like this:
This is the so-called measure-evaluate-improve-learn cycle. This process can be applied to a variety of life activities. When tuning Formula 1 car, the same technique would be used. You just need different tools.
To start the optimization cycle we need to learn about the problem This can be done in the beginning of development, after manual testing with unsatisfactory performance results. This can be a report from CI tool, which was triggered after unsafe commit. This can also be a report from users on Google Play with complaints about low performance on specific device. Such an optimization may be needed before implementing new features, which is dependant on existing, not-fast-enough functionality.
After we learned about the problem we start the optimization. In order to improve performance, we need to identify the problem. And this is the place where measuring comes handy. Android SDK has built-in capabilities for profiling. With the help of traceview and DDMS one can profile and analyze performance of the application. Apart from that, measuring is helpful for automation of testing process, by giving determined data for comparison.
After the measurements are done, detecting the bottleneck is pretty straightforward. When the problem is found, it’s time to fix it by optimizing the code. That’s where you would need all your creativity and knowledge of the domain. If you know some hardcore techniques and cheats which still maintain the sustainability of code, try them! In case of Android, think about NDK and RenderScript usage. Good idea is to check how often GC is called inside your app. On low-cost devices heap size can be surprisingly small, which will lead to often garbage collection and therefore low responsiveness. Good practice is reusing memory, instead of allocating new blocks. Also in case of intensive IO operations, asynchronous reads/writes are a must, and probabilistic variations of algorithms together with intelligent caching techniques can save the day.
After each step of improvements we should again test the application, i.e. learn if the problem is fixed. If the results are not satisfactory, the process goes all over again until the problem is gone.
Performance optimizations usually involve a decent amount of refactorings. In order to make refactorings safe it’s a good practice to cover code with tests. In this case Continuous Integration tools can greatly simplify testing and maintenance job.
Continuous Application Support
Rome wasn’t build in a day. Even though can make alpha mockups of applications in several days, a good application needs a lot more time to become mature.
When you own 1 application, you can choose couple of target devices and test your app on them. If you have 2 apps, you need twice as much time to test everything by hand. If you are an established mobile development company with a big mobile portfolio, costs for manual application testing can blow up your budget. Moreover, it’s hard to track how all the apps evolve over time.
Continuous Integration tools automate the process of application testing, deployment and maintenance. They help finding bugs early, save time and money during the lifespan of the project.
In case of Android projects, CI tools have proven their effectiveness, especially when there is a need to test applications on a variety of configurations. Currently, the most used CI for Android is Jenkins. It has an active and vibrant community of users, lots of useful plugins and is easily extensible.
Below is the proposed architecture for the private continuous integration testing environment, based on Jenkins CI:
This environment would allow:
- testing multiple device configurations
- tracking multiple projects
- measuring test execution performance over time
- analysing source code on every commit
- finding android lint issues
- automating deployment tasks
Actually, there’s almost no limit for the Jenkins CI improvements and customizations. With the help of custom plugins this system can be enriched with various kinds of actions, which automate daily DevOps routines.
Of course, there is no silver bullet and implementing CI environment also needs a lot of resources, intelligence and of course people. Tests have to be implemented by people who have programming skills, testing infrastructure has to be set up and maintained by an administrator, and whole environment needs additional hardware and software expenses. The effectiveness of automation start to show off after certain amount of manual work redundancy, which is often the case with Android development.
Keeping up with fast-changing industry standards is far from trivial. Supporting thousands of different devices with different OS versions each is indeed not easy. Supporting several apps for this market is even harder. Keeping it all fast is close to impossible. Maintaining this with every new release or bugfix is a pure nightmare. Without a right tool.
Implementing functionality changes is very similar to production of goods on factory. Every good, as every update, is designed with accuracy and intelligence. But before reaching end users each one of them passes same strict quality control, keeping the production massive, continuous and reliable.
by Ostap Andrusiv, Victor Haydin and Markiyan Matsekh