Studies have indicated that after installing a new app, only a quarter of the users return to the app on the second day. The retention rate is eminently low and the majority of the users uninstall the app after the first use. You might have guessed the reason for this. If you think the reason is scanty testing then you are correct. A lot of times testers show little interest when testing is repetitive. Although regression testing is crucial to the success of the project, it is often overlooked to some extent. So let’s understand the concept of regression testing.
What is Regression Testing
In simple terms, regression testing can be defined as retesting a computer program after some changes are made to it to ensure that the changes do not adversely affect the existing code. Regression testing increases the chance of detecting bugs caused by changes to the application. It can help catch defects early and thus reduce the cost to resolve them.
Regression testing ensures the proper functioning of the software so that the best version of the product is released to the market. Although, creating and maintaining a near-infinite set of regression tests is not feasible at all. This is why enterprises are focusing on automating most of the regression tests to save time and effort.
Types of Regression Testing
There are different types of regression testing for different phases of testing. Let’s understand the types of regression testing.
Unit Testing: It is done when coding changes are made for a single unit and a tester re-runs all previously-passed unit tests. Often automated unit tests are built into the code to make unit testing efficient.
Progressive Testing: This type of testing works effectively when there are changes done in the software/application specifications as well as new test cases are designed.
Selective Testing: In this to cut down the retesting cost and effort, testers use a subset of the current test cases. A test unit must be rerun when there are changes in the program entities it covers.
Retest-All Testing: In this, we reuse all test cases even where the changes have not been made. It shouldn’t be done where there is a small change in the application as it is very time-consuming.
Complete Testing: Complete testing is effective when multiple changes have been done in the existing code. It is done to identify unexpected bugs and the final system can be made available to the user once complete testing is done.
How to Derive a Regression Testing Plan
Whenever there is a change in the app or a new version is released the developer carries out these tests as a part of the regression testing process.
First, the developer executes unit-level regression tests to validate the code that they have modified along with any new test that is created to cover any new functionality. Then the changed code is merged and integrated to create a new build of AUT. After that smoke tests are performed to assure that the build that we have created in the previous step is good before any additional testing is performed.
These tests can be executed automatically by continuous integration services such as Jenkins. Once we get the assurance that the build is perfect then we do the sanity testing to confirm that the functionality works as expected and known defects are resolved before conducting integration testing.
Integration testing is performed to verify that the units of the apps interact with each other and with backend services such as databases. The next step is to schedule regression tests depending on the size and the scope of the code either a partial or a full regression test may take place.
Defects are reported back to the development team and many require additional runs of regression testing to confirm the resolution. So based on the test reports you can analyze and figure out what test cases can be added for the next checking process as well and you create your report on that.
Regression Testing Challenges
Automation is always a smart move but there will be some challenges that would need your attention. Let’s have a look at the challenges that you might face in automation testing.
Cost is High
In terms of business expenses, the company will have to invest a lot of time and money in repetitive testing. Also, there’s no specific return on investment that is attached to regression testing. It is a complicated process and to explain the importance of regression testing in terms of business benefits to the management is really difficult. But this has to be done to draw the budget for the testing even though it is expensive from a management point of view.
Unstructured Information
If new testers are recruited during the ongoing software testing project, either to speed up the process or to release some of the tasks. The existing testers in the organization might not have time to give complete details about the functionalities and features of the app, existing bugs, delivery time, etc. The new testers might have to initiate testing with inadequate data about the ongoing testing project. Challenges like these need an appropriate test strategy for efficient collaboration.
Time Restrictions
The focus of enterprises is on developing quality apps and delivering them faster. This is why regression testing is always time-bound. Testers always get a deadline to complete the process of regression testing. To keep pace with time, The testers might have to take chances to give more importance to the critical regression tests and skip a few tests. It is a serious challenge as it is critical to decide which tests can be skipped.
Maintenance and Optimization
A major challenge is to maintain and optimize the existing regression test suites. The existing regression test suites might need modification like adding, removing or editing the existing test cases, whenever there are new changes. All this need to be done before the deadline fixed for the regression testing. Therefore, optimization and maintenance is another challenge that needs attention.
Best Practices to Perform Regression Testing
Now that we are aware of the challenges in regression testing. Let’s have a look at the key changes we can do to enhance it.
Focus on the Frequently Used Paths
Frequently used paths also make the most frequent use cases for your app. They must include the most popular features and the basic functionality of your app. You should be aware of the core group of users and their usual interaction with the app features. The regression test cases must ensure that this core functionality is working as expected.
Update the Regression Pack Regularly
A regression pack is a collection of test cases that are performed when a new app version is released or if there are any new updates. The tests in a regression pack are created keeping in mind the app’s older version requirement specifications. Ad-hoc tests should be included in the regression pack to keep it up to date. You wouldn’t want to waste your time on tests to validate an old feature that is not included in the latest version of the app.
Create an Entry/Exit Criteria
The entry/exit criteria that we usually follow in the software development lifecycle can be implemented in regression testing as well.
As entry criteria, a fixed set of conditions need to be met like reviewing bugs and performing regression testing to fix those bugs. Exit criteria can be applied with a fixed set of conditions like making sure that all the tests are executed and no bugs remain unfixed.
Automate Regression Tests
Automating regression tests makes the process much more efficient and saves time and effort. Repetitive testing can make testers lethargic and testers might lose motivation. It also affects the quality of their work as the testing is repetitive. Automated regression testing will free up resources to work on the more complicated tests. When we use automation, the test cases along with the software can also be reused which increases the return on investment.
In a Nutshell
Change is hardest at the beginning, messiest in the middle and best at the end. This quote by Robin Sharma suits the importance of regression testing in delivering a smooth functioning app. As mentioned earlier in this blog, different types of regression testing can be performed at all stages of the testing life cycle. And there are ways to overcome the challenges in regression testing too.
One option is to use a cloud-based app testing platform which will enhance the effectiveness of regression testing by many folds. There will be hundreds of devices to perform testing in parallel and features which complement regression testing automation.
‘Metrics’. This term has become one of the widely used terms today in enterprise mobility especially for measuring mobile app performance.
Let us first understand why are the metrics are so important to analyze the performance of an app.
Generally, app developers consider mobile app performance testing as an afterthought. Mobility teams have to meet stringent deadlines and for them, testing the performance of an app has the least priority. To them, passing the functional tests meet all critical criteria for launching an app. Well, let us find out why is performance such an important criterion for mobile app testing.
Reasons why measuring Mobile App performance is such an important criterion:
1. Speed wins: As per the Compuware report, users expect their apps to load in about two seconds. This can even be proven right by the SEO reports from Google, which considers the site speed as an important parameter for Page ranking.
It is important for mobility teams to understand that your users want their apps to respond in about two seconds or less. Anything beyond two seconds, and you might lose your business.
Once the app starts taking anywhere close to five or 10 seconds, users start shifting to your competitors.
2. Mobile App Error Rates are probably much higher than it seems: You must have struggled with issues like images not loading, network errors, content feeds leaving blank holes in the app, timeouts, crashes, freezing, upload failure etc. Most of these issues are not errors of apps but caused by unreliable networks.
The error rates are highly variable and depend on network conditions. In the United States, we’ll typically see 3% error rates, whereas in India, China, and on challenging networks in Europe and Asia, we see error rates up to a frustrating 12%. Not measuring and then getting a handle on mobile app errors will quickly result in frustrated customers, uninstalls and 1-star reviews in the app stores. And no one wants that.
3. 5 star app rating: Reviews and ratings have become important aspects of judgment for any user before downloading your app. And the app should be perfect in all the ways to ensure a 5 star rating.
If you focus on functional tests and miss out on the mobile app performance not knowing, if too much battery is drained on a particular device, or is consuming too much data, then your Playstore might be full of negative reviews even before you market it well.
In this post, you’ll know how app monitoring performance works so that you can get the best out of your Smartphone software.
What is application performance management? 10 APM features that we can tick right off our fingers are:
Troubleshooting of disruption of frequent speed and uptime
Monitoring of the speed at which transactions are performed by end-users and systems
Use of a single integrated suite of software tools
Load-testing
Synthetic monitoring
Real-use monitoring
Root-cause analysis
Web-performance monitoring, to gauge app speed and uptime
An end-to-end overview of bottlenecks
Review of service interruptions
APM is integrated through the lifecycle of an application including the pre-deployment and post-deployment stages. It is therefore important for developers, testers, and business teams. A slow load can be prevalent due to a host of issues related to APIs, servers, or browsers. APM techniques are what help pinpoint the actual problem.
Basic availability monitoring involves testing IP protocols and network services, but a user needs to establish some optimal performance thresholds and real-time alerts to monitor and manage both speed and reliability.
Tips for monitoring app performance
Performance monitoring must take into account certain things in order to optimize user experience. These include measuring from the user’s point of view, i.e. performance, stability, and resource utilization in real time the way the user perceives them.
The context is crucial too. It’s important to determine how many users were affected by a slowdown, and what action on a specific operating device prompted the disruption. To truly experience a ‘mobile moment,’ you should follow these tips for monitoring your app performance:
a) Measure Frame rendering time
Only measuring network time doesn’t cut it out for users, who are interested in when the network resumes as well as when it responds.
Measuring response time by the device or operating system allows the product manager to focus on one platform at a time, respond to performance issues, and deploy resources accordingly.
b) Measure battery usage
It’s crucial to measure the battery consumption of your mobile app, along with data plan impact and memory usage. If the app is responsible for draining your battery, you should bring it in line with best practices.
Frequent updates can also be minimized so that user satisfaction can be increased.
c) Measure cell data consumption
Recent studies by Dimensional Research have shown that 20% of respondents stopped using apps that were heavy on data usage. This indicates that you should measure the consumption by app version, the total number of users, KBs of data consumed per minute, the amount of data being transferred per hit, and the percentage of data being consumed per hit.
d) Measure errors
HTTP errors from certain URLs need to be captured. This will help track the number of users who received an error, the actions that resulted in it, number of users with errors on app launch, the percentage of actions that led to at least one error, and the app version.
pCloudy helps you with performance profiling of your mobile apps. You can view the CPU usage, Memory usage, Data usage and Battery consumption while performing a series of actions on the app. The Real time mobile app performance monitoring feature helps you to keep track of the app’s performance while conducting manual tests. You can even check the performance consistency of the app by running automation test suites in parallel on multiple devices and by also simulating different network environments.
App Performance while conducting Manual Tests
The performance of an app can be viewed real-time while conducting functional tests. You can simply select the application installed on the device from the Tools section to view a graph of Memory and CPU usage, with the packets/bytes consumed in the Data Usage.
All information captured during a testing session is gathered by the platform and stored in a secure location to be used later. When a tester completes his testing session using a device, the performance related information can be found in the Cloud Drive.
Battery Consumption
The Battery Consumption graph indicates Battery consumption by Device, Battery consumption by Application and Battery consumption in %.
CPU Consumption
The CPU consumption of an App is plotted as CPU consumption in percentage against Time Duration in seconds.
Memory Consumption
The CPU consumption of an App is plotted as Memory consumption in ‘Mb’ against Time Duration in seconds.
You can listen to this webinar on Performance testing to find out how a cloud based solution can help your app perform well in all situations.
With the myriad of apps cluttering our smartphone screens nowadays, it has become all the more important to optimize app performance. While going through your daily routine, you certainly don’t want useful apps hanging up or worse still, crashing abysmally.
In this post, you’ll know how app monitoring performance works so that you can get the best out of your smartphone software.
Troubleshooting of disruption of frequent speed and uptime
Monitoring of the speed at which transactions are performed by end-users and systems
Use of a single integrated suite of software tools
Load-testing
Synthetic monitoring
Real-use monitoring
Root-cause analysis
Web-performance monitoring, to gauge app speed and uptime
An end-to-end overview of bottlenecks
Review of service interruptions
APM is integrated through the lifecycle of an application including the pre-deployment and post-deployment stages. It is therefore important for developers, testers, and business teams.
A slow load can be prevalent due to a host of issues related to APIs, servers, or browsers. APM techniques are what help pinpoint the actual problem.
Basic availability monitoring involves testing IP protocols and network services, but a user needs to establish some optimal performance thresholds and real-time alerts to monitor and manage both speed and reliability.
Tips for monitoring app performance
Source: Pixabay
Performance monitoring must take into account certain things in order to optimize user experience. These include measuring from the user’s point of view, i.e. performance, stability, and resource utilization in real time the way the user perceives them.
The context is crucial too. It’s important to determine how many users were affected by a slowdown, and what action on a specific operating device prompted the disruption. To truly experience a ‘mobile moment,’ you should follow these tips for monitoring your app performance:
Measure Frame rendering time
Only measuring network time doesn’t cut it out for users, who are interested in when the network resumes as well as when it responds.
Measuring response time by the device or operating system allows the product manager to focus on one platform at a time, respond to performance issues, and deploy resources accordingly.
Measure battery usage
It’s crucial to measure the battery consumption of your mobile app, along with data plan impact and memory usage. If the app is responsible for draining your battery, you should bring it in line with best practices.
Frequent updates can also be minimized so that user satisfaction can be increased.
Measure cell data consumption
Recent studies by Dimensional Research have shown that 20% of respondents stopped using apps that were heavy on data usage. This indicates that you should measure the consumption by app version, the total number of users, KBs of data consumed per minute, the amount of data being transferred per hit, and the percentage of data being consumed per hit.
Measure errors
HTTP errors from certain URLs need to be captured. This will help track the number of users who received an error, the actions that resulted in it, number of users with errors on app launch, the percentage of actions that led to at least one error, and the app version.
