Category: Testing Skills

Transforming Software Testing: A Journey from Basics to Specialized Excellence

Charting the Course: A New Paradigm in QA

The software landscape is ever-evolving, and in its flow, the role of Quality Assurance (QA) has burgeoned from a mere checkpoint to a strategic cornerstone. It’s a narrative of transformation, where roles within software testing are no longer confined to generic titles but are sculpted into specialized beacons of expertise.

The Genesis: More Than Just Testing

The inception of our journey was marked by a recognition that our QA team, a mosaic of potential, was marooned on an island of outdated structures. This epiphany was our call to action – a call to sculpt a robust career pathway for every tester.

The Blueprint: Values that Mold Careers

We leaned on our core tenets of curiosity, creativity, and collaboration to lay the foundation for roles that would not just exist but thrive. These values were not mere words; they were the crucibles that shaped the roles we envisaged.

The Spectrum: From Junior Pioneers to Architectural Maestros

We delineated our career paths into clear strata, each reflecting a distinct echelon of expertise:

  • Entry-Level Pioneers: Where the journey begins, our QA Interns and Testers are the bedrock, honing their skills, learning the ropes, and imbibing the essence of quality.
  • The Intermediate Vanguard: QA Engineers, the intermediaries, who with a balanced blend of skill and innovation, take on more intricate aspects of testing, curating quality with each test case.
  • The Senior Strategists: Senior QA Engineers, the specialized craftsmen, wielding tools of automation and analytics, architecting the frameworks that uphold our standards.
  • Leadership and Visionaries: QA Leads and Managers, the navigators of our QA odyssey, charting the course, steering through complexities, and anchoring the team to its goals.
  • The Specialized Elite: Performance and Security Experts, the guardians of our software’s integrity, ensuring each product can withstand the tides of demand and the shadows of cyberspace.

The Revelation: Roles That Resonate

Each role now resonates with a defined purpose, echoing our values and encapsulating the skills and responsibilities pertinent to that stage of growth. This clear delineation has not only catalyzed professional growth but has also enhanced the quality of our products.

The Vanguard: SMEs as Beacons of Mastery

Subject Matter Experts (SMEs) in Test Analysis, Performance, and Security have emerged as the vanguards of their domains. These roles are not just titles but are sanctuaries of expertise, each SME a beacon guiding their peers towards excellence.

The Odyssey: A Cultural Metamorphosis

The rollout of these roles was not just a structural change; it was a cultural metamorphosis. A once rigid hierarchy gave way to a dynamic ecosystem where each role is a stepping stone to a zenith of specialized prowess.

The Harvest: A Resounding Triumph

This transformation has been a resounding triumph, with our QA team not just meeting but redefining industry benchmarks. The clarity in career progression has unfurled potential into performance, and ambition into achievement.

The Continuum: A Pledge to Perpetual Evolution

Our journey doesn’t plateau here. We pledge to perpetually evolve, iterating on our structures and roles, ensuring our team is not just current but cutting-edge, not just testers but trailblazers.

An Invitation: Let’s Converse and Collaborate

Are you on a similar voyage? Are you considering charting such waters? I am eager to share insights, and strategies, and perhaps, learn from your narratives too. Let’s collaborate to elevate QA beyond its traditional bastions, nurturing careers that transcend expectations.

Using AI to Improve Test Efficiency: A Practical Example of Automated Test Case Generation

Software testing is a critical part of the software development process, ensuring that the application meets the requirements and functions as intended. However, the traditional manual test case generation process can be time-consuming, and tedious, and may not cover all possible scenarios. This is where AI comes in, automating the test case generation process and improving efficiency. In this article, we will explore a practical example of how AI can be used to generate test cases automatically, improving test efficiency.

Example Scenario:

Imagine we have a web-based e-commerce application that enables users to search for and purchase products. The application has several features, including user registration, product search, product details, and checkout. A team of developers is working on the application, and a team of testers is responsible for ensuring that the application works as expected and meets the requirements.

Manual Test Case Generation:

Traditionally, testers create test cases manually based on their knowledge of the application and the requirements. For our example e-commerce application, testers would need to create test cases to cover each functionality, such as registering a user, searching for a product, viewing product details, and placing an order. The test cases would need to cover different scenarios, such as valid and invalid inputs, error handling, and boundary conditions.

Automated Test Case Generation:

However, with AI, we can automate the test case generation process by using machine learning algorithms to analyze the application code and generate test cases automatically. In this example, we will use an open-source tool called “AutoTestGen,” which is based on machine learning algorithms.

AutoTestGen works by analyzing the code of the application and generating test cases to cover all possible paths and branches. The tool can generate test cases for various programming languages, including Java, C++, and Python. To use AutoTestGen for our e-commerce application, we would provide the source code to the tool and run it. The tool would then analyze the code and generate test cases automatically, covering all possible paths and branches.

Comparison:

To compare the results obtained using manual test case generation versus AI-based test case generation, we can measure the time and coverage. Manual test case generation is time-consuming and may not cover all possible scenarios. AI-based test case generation, on the other hand, is faster and more comprehensive, covering all possible paths and branches.

Conclusion:

Automated test case generation using AI can significantly improve test efficiency by automating the tedious and time-consuming process of manual test case generation. It can also improve the coverage of test cases by covering all possible paths and branches. Open-source tools such as AutoTestGen can be used to automate the test case generation process, improving test efficiency, and ensuring better software quality. With the help of AI, software development teams can save time, reduce costs, and improve their testing process, ultimately leading to better software quality and user experience.

If you’re interested in learning more about how AI can improve software test efficiency or want to consult with me to help introduce AI to your testing teams, please feel free to reach out to me at askabk@abkhalid.com . Together, we can explore how AI can transform the way we test software and help us deliver high-quality products faster and more efficiently.

Hey, What do you call a group of programmers in a bar?

A stack overflow

You need to add a widget, row, or prebuilt layout before you’ll see anything here. 🙂

How AI Can Help Improve Software Test Efficiency

As technology evolves, software testing becomes more complex and time-consuming. However, with the advent of artificial intelligence (AI), testing can be done more efficiently and effectively.

  1. AI can automate repetitive and mundane testing tasks AI-powered testing tools can be trained to run tests repeatedly without tiring, making them ideal for running large volumes of automated test cases. By automating these repetitive and time-consuming tasks, we can dramatically improve our productivity, accuracy, and speed to market. For example, AI can help automate the testing of web applications, which often involves repetitive tasks like logging in and out of the application or filling out lengthy forms.
  2. AI can identify defects more accurately and quickly AI can help improve the accuracy and speed of defect detection and classification, enabling faster and more effective remediation. For example, AI can be used to analyze log files to identify and diagnose performance bottlenecks, detect security vulnerabilities, and pinpoint the root cause of failures in real time.
  3. AI can enhance test coverage and accuracy AI can help improve test coverage by identifying areas that are more likely to be problematic and testing them thoroughly. For example, AI can be used to identify the most frequently used functions and features of an application and prioritize testing efforts accordingly. This approach can help reduce the risk of bugs and improve overall product quality.
  4. AI can enable predictive testing AI can be used to predict the likelihood of defects and failures in the software, enabling proactive testing. For example, machine learning models can be trained to detect patterns and anomalies in application data, allowing testers to identify potential issues before they become major problems.

Now, for a joke! Why did the programmer quit his job? He didn’t get arrays!

But in all seriousness, the benefits of implementing AI in software testing are no laughing matter. Here are some numbers to back it up:

  • According to a report by MarketsandMarkets, the AI in software testing market is expected to reach $1.5 billion by 2022, growing at a CAGR of 33.7%.
  • In a case study by Infosys, a large healthcare provider was able to reduce their testing efforts by 20% and improve their test coverage by 90% by using AI-powered testing tools.
  • A study by Capgemini found that AI-enabled testing can reduce testing time by up to 20% and improve test coverage by up to 80%.

If you’re interested in learning more about how AI can improve software test efficiency or want to consult with me to help introduce AI in your testing teams, please feel free to reach out to me at askabk@abkhalid.com. Together, we can explore how AI can transform the way we test software and help us deliver high-quality products faster and more efficiently.

And if all else fails, at least we can use AI to tell us a good joke or two along the way.

Why only Load Runner is not enough for Performance Testing

I just got email form my manger (she is a bit blog enthusiast) that I have stopped writing blogs. The thing is at Kualitatem, we encourage our resources to learn and/or share their experiences. I played the usual busy card, but later on her persuasion, here I am.

This topic I have been having in my mind for more than 6 months. And I not so called ‘performance tester’ was not able to get the proper answer from anybody. Question was, “Is load runner able to give you the actual picture on its own”. Well simple answer is, “If you are a guru, then it gives you fair idea of the whole situation, but no, it’s not possible”. (To let you on a secret, my ego was hurt by couple of load runner projects, which made me sit up and take notice, before that I always thought load testing as alien for myself, a test management resource.)

I will try to explain below why only load runner is not enough for performance testing, below.

Load runner is a tool for performance monitoring. It is able to tell you, what is application’s behavior, at what time under how much load. But it will not be able to tell you what was the origin of the problem and was this problem even problem, or mare hardware configuration issues. (Even load runner itself says that it is able to only provide 10% of actual diagnostics information.)

After exploring more, and doing some experiments, I came to know load runner is not been able to tell me

  • What was the condition of hardware before and after load test
  • What was the area causing the problem
  • What other application area are suffering due to this load
  • Is there a way to improve (clue, people say that you only need to look at page load to understand performance of application, well my answer now is HELL NO!)

So we have identified the main problems, we need to find the solution for it.

My research led me to the solution, and believe you me, the solutions was always there, we (or Ancient Performance Testers) only chose to let us believe we only have Load Runner, so coming back to solution, it’s simple, we need to use more tools for Performance Testing together with different scopes in order to establish better results from performance testing, and HP is already providing them by name of HP Diagnostics and HP Site Scope.

Well Site scope is tool basically monitor the all aspects of IT infrastructure (in words of HP, “Remote monitoring of IT infrastructure and applications without installing any software on target systems”) while name of diagnostics clearly indicates its function, which is to deliver to you actual problem at any given point of time, and helps you in targeting them (in words of HP, “Remote monitoring of IT infrastructure and applications without installing any software on target systems”).

Now again the problem is to know at which point of time, what is right information. So consider following chart

  • Site scope will give you information of hardware and software for 24/7, when script is being executed, before it and after it.
  • Diagnostics will tell you what is the current state of application, which function is using how much resources, and what has left stones unturned
  • Loadrunner will execute the scenario, and tell you what is the situation form user point of view during that scenario

So by combining the results from all three, we will be able to identify the situation before the issue was occurred, what occurred that issue, and from where we can resolve it.

In my next blogs, I will try to help you set up performance testing environment, as I believe is the correct way.

Topics are

  1. HP Diagnostics introduction
  2. Setting up Diagnostics Commander Server
  3. Setting up Diagnostics Mediator Server
  4. Setting up Diagnostics Java Probe with Tomcat
  5. Setting up Diagnostics Java Probe with JBOSS
  6. Setting up Diagnostics .NEt Probe
  7. HP Sitescope Introduction
  8. Setting up Sitescope monitors
  9. Configuration of HP Diagnostics and HP Site Scope with HP Load Runner

Myth about Skill Required for Automation Testing

My recent observations of SQA Skills trends in Pakistan in particular and overall in general, a tendency to ask people being hired for SQA positions if they have experience, skills or knowledge of automation testing. And the answer the hiring body is mostly looking for is if they have working experience of QTP, Selenium or RFT. If the resource says he is willing to learn QTP or Selenium or RFT or whatever, people start teaching them these tools.

I have started to get frustrated by this behavior, but I also have been part of this culture too, so one can say I am frustrated with myself too.

Let me quote you one story, one person who was well-versed in Software Quality Management in early 2000(s); advertised following skills for QA resources, job advertised with title Software Developer and Quality Analyst

  • ISO
  • Development (language not important)
  • Automata
  • Data Communication and Networks
  • Regular Expressions
  • Critical Eye

After experience of hiring, getting hired, working and managing teams, I have to come to know that above ad was the best possible ad for what we mostly look from our resource.

But are we looking at right skill set, or even we know what we want, I think about 90% people don’t know the answer, and the ones that know hide behind ‘budget limitation’ wall.

I believe we should look at software automation testing as we look at software development; we need at least two type of personal, one Software Automation Script Developer and one Software Automation Engineer.

Software Automation Engineer: Should be the person, who has application domain knowledge, has written the Test Cases for the application, and understands which area of application needs regression. He also understands and can write suede code, which a developer can understand and convert to physical code. He is also going to prepare the data for the data driven automation testing.

Software Automation Script Developer: Should be the person who gets the test case in document form, and just creates the script as defined. He has the understanding of the scripting language.

I will try to explain in detail what these roles should comprehend in grand scheme of Software Quality Assurance in the long run.

Industrial Mobile Application Testing

Introduction

Mobile applications still have a largely horizontal character, but new developments and capabilities are beginning to show how certain vertical markets can gain unique business benefits from mobility. The growth of mobility-enabled applications is driven essentially by the same factors that are driving IT and business process change, namely the need to be more responsive, optimize the efficiency of staff resources, and shorten the cycle time of key processes throughout their value chain.

Another driver of industry-specific mobile application adoption is the evolution of the technology, including the development of platforms such as the Research in Motion, Android, Windows Mobile and iOS, whose feature enables the extension of enterprise applications to mobile employees as well as improvements in security and the availability of applications from major ISVs.

Defining Industry Oriented Mobile Applications

The key distinction of Industry Mobile Applications is that they facilitate one or two major processes of industry instead of whole application. So set of similar stakeholders are given a similar applications. Some are transactional and order-entry component, while other might contain only the results and graphs, some required advanced content validation and verification others help in remote data manipulation.

All this is because industry is taking a turn where need for

  • More responsiveness to their customers
  • More facilitation of collaboration, operations and management environment
  • Optimization of efficiency
  • Shorten the cycle of key processes
  • Availability of information on the go

Challenges in Mobile App Testing

It’s been clear for a while that mobile devices are the current market players, even more so that some experts have been counting on them to take over the PCs and Desktops in near future. But as with any emerging technology, developing and implementing mobile applications can pose a number of unique challenges.

Mobile applications, although have limited computing resources, are often built to be as agile and reliable as PC-based applications. In order to meet the challenge, mobile application testing has evolved as a separate stream of testing.

By missing the major user characterizations, mobile application lose the “gloss” within first couple of months and therefore, user retention period for mobile applications is very low, only around 10% users are found still using the same mobile application after six months of its download.

Mobile application user retention over period

Many people have pointed fingers at many aspects and loop holes in Mobile App Testing, some of which are mentioned below.

  1. The major challenge in Mobile App Testing is the multiplicity of mobile devices with different capabilities, features and restrictions. Devices may have different technical capabilities such as amount of available memory, screen resolution, screen orientation and size of the display, network connectivity options, support for different standards and interfaces.
  2. Many mobile solutions involve a significant hardware element in addition to the PDA, such as scanners, mobile telephony, GPS and position based devices, telemetry, etc… These extra hardware elements place additional demands on the tester, particularly in terms of isolating a bug to hardware or software.
  3. Mobile applications are often intended to be used by people with no technical or IT background; such as meter readers, milkmen, insurance sales people; on devices that have small screens, and no or awkward keyboards. Good usability testing, carried out in conjunction with key users, in their own environment, is essential.
  4. There are multiple operating systems that are prevalent in the mobile space like Symbian, Android, iPhone OS, Windows, Linux, Blackberry OS, palm OS, Brew, etc. Each of the operating systems can have further versions for different types of devices which makes platform testing complex and further challenging
  5. Another challenge is that the developers need to focus on developing applications that are easy to use on a mobile and consume less power.

The Most important aspect that our analysis, development and testing teams often miss is that mobile application development takes a lot less time duration then mobile application testing, compared to the conventional model where application analysis & development takes more time precedence over testing. We therefore, deliberately tend to give less time for testing which might result in the application starting to lose out to competition over time. Due to this misunderstanding and thus improper testing strategy in mobile application; growing number of mobile applications are being taken off-app store every month, just in September 2011 alone following stats were witnessed

Percentage of applications taken off the stores

These trends show, that we can never use the same testing methodologies as we have been using on the conventional web and desktop applications, we have to devise a new strategy and methodology, which is going to take into account what actually is the mobile world, what it constitutes of and the adjustments it calls for in our conventional testing patterns and strategies.

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