QA Automation

Software Quality and User Satisfaction Guaranteed

Software application engineers and developers are at the center of the modern finance ecosystem, why mire them in tedious and repetitive testing tasks, when QA automation leveraging the key principles of DevOps and Agile methodology can enhance the efficiency of software testing?

As the scope of an application or software expands, for most QAs staying in control poses the biggest challenges, as there is either a surfeit of tests or a deficit of tests. Both instances are undesirable. Because if you can’t run a comprehensive array of tests, you will end up bugs and vulnerabilities passing downstream and a poor-quality product, and alternatively if you fail to reduce the number of tests or work them out quickly, you might end up with massive pile up of tests – a bloated CI/CD pipeline. And when this happens, you might just end up “putting your hands up in the air” and demanding a postponement of the release. Much to the chagrin of the business owners who will lose precious time and competitive advantage.   

The Kind of Tests Programmers Run during SDLC

The job of programmers or QAs is not easy, they run reams and reams of codes, write codes from scratch, and decipher the gaps/bugs/vulnerabilities with little help apart from the coding libraries and their experienced seniors. As a runup to the SDLC, a programmer or a QA will run tests such as:  

  • Integration tests: for finding out whether the components merge in the larger subsystem; interact with other components and whether the data flow is smooth or not, along with testing the interfaces and contracts for compatibility, environment testing, and testing scenarios. 
  • Smoke tests: a smoke test checks if the basic functionalities of the software are working properly before proceeding with more comprehensive testing. 
  • Release to beta users: gather valuable insights, identify and address issues early, and ensure that the final release meets user expectation  
  • Regression testing: The purpose of regression testing is to verify that previously tested and working features of the software continue to function correctly after modifications or enhancements have been made. It helps ensure that changes in one part of the software do not adversely impact other parts.  

Most of the time the tests that are underlined above run in parallel. As there are a million things that programmers or QA teams must address at one and the same time, the chances of queues building up when you are working manually or have not invested in an end-to end automated system, for facilitating testing, are inordinately high. 

Testing queues: An emblematic problem with software testing

Software testing queues present a notable challenge in the realm of testing, causing difficulties for programmers when juggling multiple tasks and determining priorities. They face the dilemma of whether to focus on resolving the existing test issues or addressing the previously identified bug that exposes a vulnerability, all the while contending with overflowing pipelines. Alongside the issue of bloated testing, additional concerns arise from problematic code stemming from legacy systems and a lack of documentation, insufficient resources, and strict deadlines, resulting in unnecessary apprehension. Furthermore, programmers are burdened with handling environmental matters, such as configuring a test environment to resemble the production environment, grappling with intricate and hard-to-isolate bugs, and effectively managing test data. These manual processes consume a significant amount of time. Hence, there arises a necessity for a platform that combines artificial intelligence/machine learning (AI/ML) and robotic process automation (RPA) to expedite the testing process while ensuring efficiency, transparency, and accuracy.

Meet the QA Companion: Turbocharging the SDLC 

If there is an area where AI is silently revolutionizing, it is the software development lifecycle, encompassing not only coding tasks but also quality assurance (QA). According to a McKinsey study, generative AI can accelerate coding tasks by up to twice the speed, while AI’s role in QA serves as a key differentiator, ensuring quality, transparency, and timeliness.

Magic FinServ’s QA companion, powered by AI, addresses all testing challenges throughout the SDLC. This intelligent solution takes care of tedious, repetitive, and complex testing aspects. With its exceptional intelligence and intuitive nature, it automates tasks, enabling developers to build high-quality software and achieve faster and more reliable release cycles, even when dealing with extensive test suites and challenging tests. Moreover, it provides actionable insights based on its built-in intelligence.

  • By efficiently handling humongous amounts of data, surpassing previous capabilities, the QA companion mitigates risks and reduces unnecessary redundancy simultaneously.
  • AI-assisted testing eliminates friction between teams.
  • The AI-assisted QA Companion takes on most of the demanding tasks, resulting in a highly enriching and empowered developer experience, promoting workplace efficiency.
  • Lastly, developers no longer need to rely on more experienced colleagues for assistance, as QA can fulfill their needs by explaining new concepts, synthesizing information, and providing step-by-step guides.

Unique selling points of Magic FinServ’s QA Companion 

The QA companion serves as a reliable aid in navigating the SDLC, offering automation and intelligent code generation that far surpasses human capabilities in terms of time efficiency. Here are some key features that highlight the value of the QA companion for programmers and QA teams:

  • Effortlessly track and communicate testing progress in a highly effective manner with the help of the QA companion.
  • Seamlessly process data from various file types, including handwritten notes.
  • Utilize the in-built test scenario generator to produce intelligent test cases and scenarios, ensuring the pursuit of only necessary test cases and preventing test bloating.
  • Download AI-generated test cases and scenarios in any desired file format, customizable to meet user output requirements.
  • Generate automated scripts and eliminate the tedium associated with writing code from scratch multiple times.
  • Manual scripting becomes unnecessary as the QA companion supports a wide range of scripting languages, such as Python, Java, JavaScript, C++, and more.
  • Experience the benefits of a streamlined testing process within agile workflows, integrated with Agile tools like JIRA.
  • Leverage the code optimizer’s smart detection technology to identify best practices, code smells, and vulnerabilities.
  • Generate high-quality reports, including test plans, test strategies, execution reports, and more.
  • Ensure continuous testing throughout your development pipeline, guaranteeing quality at every stage.
  • Accelerate time to market by shortening project/product deployment times, gaining a competitive edge in the market.

Now you can bid goodbye to all the juggling between tests as the QA companion not only allows you to carry out tests in parallel tests without getting lost, but it also ensures that you are in control when it comes to timelines and releases. For a more comprehensive discussion on what the QA Companion can do  and its role in Financial Quality Assurance and Quality Assurance in Financial Services, write to us at

APIs are driving innovation and change in the Fintech landscape with Plaid, Circle, Stripe, or Marqueta, facilitating cheaper, faster, and more accessible financial services to the customer. However, while the APIs are the driving force in the fintech economy, there is not much relief for the software developers and quality analysts (QAs). Their workloads are not automated and there is increasing pressure to release products to the market. Experts like Tyler Jewell, managing director of Dell Technologies Capital, have predicted that there will be a Trillion programmable endpoints soon. It would be inconceivable then to carry out manual testing of APIs as is done by most organizations today. An API conundrum will be inevitable. Organizations will be forced to choose between quick releases and complete testing of APIs. If you choose a quick release, you might have to deal with technical lags in the future and rework. Failure to launch a product in time could lead to a loss of business value.

Not anymore. For business-critical APIs that demand quick releases and foolproof testing, Automation saves time and money and ensures quicker releases. To know more read on.

What are APIs and the importance of API testing

API is the acronym for Application Programming Interface, which is a software intermediary that allows two applications to talk to each other. APIs lie between the application and the web server, acting as an intermediary layer that processes data transfer between systems.

Visual representation of API orientation

Is manual testing of APIs enough? API performance challenges

With the rise in cloud applications and interconnected platforms, there’s a huge surge in the API-driven economy.

Today, many of the services that are being used daily rely on hundreds and thousands of different interconnected APIs – as discussed earlier, APIs occupy a unique space between core application microservices and the underlying infrastructure.

If any of the APIs fails the entire service will be rendered ineffective. Therefore, API testing is mandatory. When testing for APIs, the key tests are as depicted in the graphic below:

So, we must make sure that API tests are comprehensive and inclusive enough to measure the quality and viability of the business applications. Which is not possible manually.

The API performance challenges stem primarily due to the following factors:

  • Non-functional requirements during the dev stage quite often do not incorporate the API payload parameters
  • Performance testing for APIs happens only towards the end of the development cycle
  • Adding more infrastructure resources like more CPU or Memory will help, but will not solve the root cause

The answer then is automation.

Hence the case for automating API testing early in the development lifecycle and including it in the DevSecOps pipeline. The application development and the testing teams must also make an effort to monitor API performance the way monitor the application (from Postman and Manage Engine right up to AppDynamics) and also design the core applications and services with API performance in mind – questioning how much historical data a request carries and whether the data sources are monolith or federated.

Automation of APIs – A new approach to API testing

Eases the workload: As the number of programmable endpoints reaches a trillion (in the near future), the complexity of API testing would grow astronomically. Manually testing APIs using home-grown scripts and tools and open-source testing tools would be a mammoth exercise. Automation of APIs then would be the only answer.

Ensures true AGILE and DevOps enablement: Today AGILE and the ‘Shift Left’ approach have become synonymous with the changing organizational culture that focuses on quality and security. For true DevOps enablement, CI/CD integration, and AGILE, an automation framework, that can quickly configure and test APIs is desired instead of manual testing of APIs.

Automation simplifies testing: While defining and executing a test scenario, the developer or tester must keep in mind the protocols, the technology used, and the layers that would be involved in a single business transaction. Generally, there are several APIs working behind an application which increases the complexity of testing. With automation, even complex testing can be carried out easily.

Detects bugs and flaws earlier in the SDLC: Automation reduces technical work and associated costs by identifying vulnerabilities and flaws quickly saving monetary losses, rework, and embarrassment.

Decreases the scope of security lapses: Manual testing increased the risk of bugs going undetected and security lapses occurring every time the application is updated. However, with automation, it is easier to validate if any update in software elicits a change in the critical business layer.

Win-win solution for developers and business leaders: It expedites the release to market, as the API tests can validate business logic and functioning even before the complete application is ready with the UI. Resolving thereby the API conundrum.

Magic FinServ’s experience in API engineering, monitoring, and automated QA

Magic FinServ team with its capital markets domain knowledge and QA automation expertise along with industry experience helps its clients with:

  • Extraction of data from various crypto exchanges using opensource APIs to common unified data model covering the attributes for various blockchains which helps in:
    • Improved stability of the downstream applications and data warehouses
    • Eliminates the need for web scraping for inconsistent/protected data – web scraping is prevented by 2FA often
    • Use of monitored API platform improved data access and throughput and enabled the client to emerge as a key competitor in the crypto asset data-mart space
  • Extraction of data from various types of documents using Machine/AI learning algorithms and exposing this data to various downstream systems via a monitored and managed API platform
  • Use of AI to automate Smart Contract based interfaces and then later repurpose these capabilities to build an Automated API test bed and reusable framework
We also have other engineering capabilities as:
  • New generation platforms for availability, scalability and reliability for various stacks (Java/.NET/Python/js) using Microservices and Kubernates
    • Our products built uses the latest technology stack in the industry in terms of SPA (Single Page Application) (Automated pipelines/Kubernetes Cluster/Ingres controller/Azure Cloud Hosted) etc.
  • Full stack products in full managed capacity covering all the aspects of products (BA/Development/QA)

APIs are the future, API testing must be future-ready

There’s an app for that – Apple

APIs are decidedly the future of the financial ecosystem. Businesses are coming up with innovative ideas to ease payments, banking, and other financial transactions. For Banks and FinTechs, API tests are not mere tests, these are an important value add as they bring business and instill customer confidence, by ensuring desired outcomes always.

In this blog, part 1 in the series of blogs on Automation in API testing, we have detailed the importance of Automation in API testing. In the blogs that follow, we will have a comprehensive account of how to carry out tests, and the customer success stories where Magic FinServ’s API Automation Suite has provided superlative results. Keep looking out in this space for more! You can also write to us

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