Network Engineering

Regression Testing in CI/CD and its Challenges

The introduction of the (Continuous Integration/Continuous Deployment) CI/CD process has strengthened the release mechanism, helping products to market faster than ever before and allowing application development teams to deliver code changes more frequently and reliably. Regression testing is the process of ensuring that no new mistakes have been introduced in the software after the adjustments have been made by testing the modified sections of the code as well as the parts that may be affected by the modifications. The Software Testing Market size is projected to reach $40 billion in 2020 with a 7% growth rate by 2027. Regression testing accounted for more than 8.5 percent of market share and is expected to rise at an annual pace of over 8% through 2027 as per the reports stated by the Global Market Insights group.

The Importance of Regression Testing

Regression testing is a must for large-sized software development teams following an agile model. When many developers are making multiple commits frequently, regression testing is required to identify any unexpected outcome in overall functionality caused by each commit, CI/CD setup identifies that and notifies the developers as soon as the failure occurs and makes sure the faulty commit doesn’t get shipped into the deployment.

There are different CI/CD tools available, but Jenkins is widely accepted because of being open source, hosts multiple productivity improvement plugins, has active community support, and can set up and scale easily. Source Code Management (SCM) platforms like GitLab and GitHub are also providing a good list of CI/CD features and are highly preferred when the preference is to use a single platform to manage code collaboration along with CI/CD.

Different level of challenges needs to be overcome when CI/CD setup is handling multiple software products with different teams, is using multiple SCMs like GitLab, GitHub, and Perforce, is required to use a cluster of 30+ high configuration computing hosts consisting of various operating systems and handling regression job count as high as 1000+. With the increasing complexity, it becomes important to have an effective notification mechanism, robust monitoring, balanced load distribution of clusters, and scalability and maintenance support along with priory management. In such scenarios, the role of the QA team would be helpful which can focus on CI/CD optimization and plays a significant part in shortening the time to market and achieving the committed release timeline.


Let us see the challenges involved in regression testing and how to overcome them in the blog ahead.

Effective notification mechanism

CI/CD tool like Jenkins provides plugin support to notify a group of people or a specific set of team members who are responsible to cause unexpected failures in the regression testing. Email notifications generated out of plugins are very helpful to bring attention to the underlying situation which needs to be fixed ASAP. But when there are plenty of such email notifications flooding the mailbox, it becomes inefficient to investigate each of them and has a high chance of being missed out. To handle such scenarios, a Failure Summary Report (FSR) highlighting new failures becomes helpful. FSR can further have an executive summary section along with detailed summary sections. Based on the project requirement, one can integrate JIRA, Jenkins links, SCM commit links, and time stamps to make it more useful for developers as the report will have all required references in a single document. FSR can be generated once or multiple times a day based on project requirements.

Optimum use of computing resources

When CI/CD pipelines are set up to use a cluster of multiple hosts with high computing resources, it is expected to have a minimum turnaround time of a regression run cycle with maximum throughput. To achieve this, regression runs need to be distributed correctly across the cluster. Workload management and scheduler tools like IBM LSF, and PBS can be used to run the jobs concurrently based on available computing resources at a given point in time. In Jenkins, one can add multiple slave nodes to distribute jobs across the cluster to minimize the waiting time in the Jenkins queue, but this needs to be done carefully based on available computing power after understanding the resource configuration of slave hosting servers, if not done carefully can result into node crash and loss of data.

Resource monitoring

To support the growing requirement of CI/CD, while scaling one can easily be missed to consider the disk space limitations or cluster resource limitations. If not handled properly, it results in CI/CD node crashes, slow executions, and loss of data. If such an incident happens when a team is approaching an import deliverable, it becomes difficult to meet the committed release timeline. Robust monitoring and notification mechanism should be in place to avoid such scenarios. One can-built monitoring application which continuously monitors the resources of each computing host, network disk space, and local disk space and raises a red flag when the set thresholds are crossed.

Scalability and maintenance

When regression job count grows to many 1000+, it becomes challenging to maintain them. A single change if manually needs to be done in many jobs becomes time-consuming and error-prone. To overcome this challenge, one should opt for a modular and scalable approach while designing test procedure run scripts. Instead of writing steps in CI/CD, one can opt to use SCM to maintain test run scripts. One can also use Jenkins APIs to update the jobs from the backend to save manual efforts.

Priority management

When regression testing of multiple software products is being handled in a single CI/CD setup, priority management becomes important. Pre-merge jobs should get prioritized over post-merge jobs, this can be achieved by running pre-merge jobs on a dedicated host by providing separate Jenkins slave and LSF queue. Post-merge Jenkins jobs of different products should be configured to use easy-to-update placeholders for Jenkins slave tags and LSF queues such that priorities can be easily altered based on which product is approaching the release.

Integration with third-party tools

When multiple SCMs like GitLab/GitHub and issue tracking tools like JIRA are used, tacking commits, MRs, PRs, and issue updates help the team to be in sync. Jenkins integration with GitLab/GitHub helps in reflecting pre-merge run results into SCM. By integrating an issue tracker like JIRA with Jenkins, one can create, and update issues based on run results. With SCM tools and JIRA integration, issues can be auto-updated on a new commit and PR merges.

Not only must regression test plans be updated to reflect new changes in the application code, but they must also be iteratively improved to become more effective, thorough, and efficient. A test plan should be viewed as an ever-evolving document. Regression testing is critical for ensuring high quality, especially as the breadth of the regression develops later in the development process. That’s why prioritization and automation of test cases are critical in Agile initiatives.

At Softnautics, we offer Quality Engineering Services for both software and embedded devices to assist companies in developing high-quality products and solutions that will help them succeed in the marketplace. Embedded and product testing, DevOps and test automation, Machine Leaning Application/Platform testing and compliance testing are all part of our comprehensive QE services. STAF, our in-house test automation framework, helps businesses test end-to-end products with enhanced testing productivity and a faster time to market. We also make it possible for solutions to meet a variety of industry standards, like FuSa ISO 26262, MISRA C, AUTOSAR, and others.

Read our success stories related to Quality Engineering services to know more about our expertise in the domain.

Contact us at for any queries related to your solution or for consultancy.

Author: Toral Mevada

Toral is a manager at Softnautics and has total of 11+ years of experience in quality engineering of Embedded Systems and DSP software platforms. In her career, she has worked on numerous QA and Automation projects, test framework development, and DevOps projects. She is passionate about achieving optimum process automation and developing productivity improvement tools. While not working she likes to travel and read.

FPGA Market Trends With Next-Gen Technology

Due to their excellent performance and versatility, FPGAs (Field Programmable Gate Arrays) appeal to a wide spectrum of businesses. Also, it has the feature of adopting new standards and modifying hardware as per the specific application requirement even after it’s been deployed for usage. ‘Gate arrays,’ on the other hand, relate to the architecture’s two-dimensional array of logic gates. FPGAs are used in several applications where complicated logic circuitry is required and changes are expected. Medical Devices, ASIC Prototyping, Multimedia, Automotive, Consumer Electronics, and many other areas are covered by FPGA applications. In recent years, market share and technological innovation in the FPGA sector is growing at a rapid speed. FPGAs offer benefits for Deep Learning and Artificial Intelligence based solutions, including an improved performance with low latency and high throughput, and power efficiency. According to Mordor Intelligence, the global FPGA market was valued at USD 6958.1 million in 2021, and it is predicted to reach USD 11751.8 million by 2027, with a CAGR of 8.32 percent from 2022 to 2027.

FPGA Design Market Drivers

Global Market Drivers

Let’s look at some interesting real-world applications that can be built using TensorFlow Lite on edge TPU.


The FPGA market is highly contested due to economies of scale, the nature of product offerings, and the cost-volume metrics favouring firms with low fixed costs. According to the size, 28nm FPGA chips are expected to grow rapidly because they provide high-speed processing and enhanced efficiency. These features have aided its adoption in a variety of industries, including automobiles, high-performance computing, and communications. The consumer electronics sector appears to be promising for FPGA since rising spending power in developing countries contributes to increased market demand for new devices. FPGAs are being developed by market players for use in IoT devices, Natural Language Processing (NLP), based infotainment, multimedia systems, and various industrial smart solutions. Based on the application requirement, either low-end, mid-range or high-end FPGA configurations are selected.

FPGA Architecture Overview

The general FPGA architecture design consists of three types of modules. They are I/O blocks, Switch Matrix, and Configurable Logic Blocks (CLB). FPGA is a semiconductor device made up of logic blocks coupled via programmable connections.

FPGA Architecture


The logic blocks are made up of look-up tables (LUTs) with a set number of inputs and are built using basic memory such as SRAM or Flash to hold Boolean functions. To support sequential circuits, each LUT is connected to a multiplexer and a flip-flop register. Similarly, many LUTs can be used to build for handling complex functions. As per the configurations FPGAs are classified into three types low-end, Mid-end & High-end FPGAs. Artix-7/Kintex-7 series from Xilinx, ECP3, and ECP5 series from Lattice semiconductor are some of the popular FPGA designs for low power & low design density. Whereas Virtex family from Xilinx, ProASIC3 family from Microsemi, Stratix family from Intel are designed for high performance with high design density.

FPGA Firmware Development

Since the FPGA is a programmable logic array, the logic must be configured to match the system’s needs. Firmware, which is a collection of data, provides the configuration. Because of the intricacy of FPGAs, the application-specific purpose of an FPGA is designed using the software. The user initiates the FPGA design process by supplying a Hardware Description Language (HDL) definition or a schematic design. VHDL (VHSIC Hardware Description Language) and Verilog are two commonly used HDLs. After that, the next step in the FPGA design process is to develop a netlist for the FPGA family being used. This is developed using an electronic design automation program and outlines the connectivity necessary within the FPGA. Afterward, the design is committed to the FPGA, which allows it to be used in the (ECB) electronic circuit board for which it was created.

Applications of FPGA

FPGAs in automobiles are extensively used in LiDAR to construct images from the laser beam. They’re employed in self-driving cars to instantly evaluate footage for impediments or the road’s edge for obstacle detection. Also, FPGAs are widely used in car-infotainment systems for reliable high-speed communications within the car. They enhance efficiency and conserve energy.

Tele-Communication Systems
FPGAs are widely employed in communication systems to enhance connectivity and coverage and improve overall service quality while lowering delays and latency, particularly when data alteration is involved. Nowadays FPGA is widely used in server and cloud applications by businesses.

Computer Vision Systems
These systems are becoming increasingly common in today’s world. Surveillance cameras, AI-bots, screen/character readers, and other devices are examples of this. Many of these devices necessitate a system that can detect their location, recognize things in their environment, and people’s faces, and act and communicate with them appropriately. This functionality necessitates dealing with large volumes of visual data, constructing multiple datasets, and processing them in real-time, this is where FPGA accelerates and makes the process much faster.

The FPGA market will continue to evolve as the demand for real-time adaptable silicon grows with next-gen technologies Machine Learning, Artificial Intelligence, Computer Vision, etc. The importance of FPGA is expanding due to its adaptive/programming capabilities, which make it an ideal semiconductor for training massive amounts of data on the fly. It is promising for speeding up AI workloads and inferencing. The flexibility, bespoke parallelism, and ability to be reprogrammed for numerous applications are the key benefits of using an FPGA to accelerate machine learning and deep learning processes.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at for any queries related to your solution or for consultancy.


Author: V Srinivas Durga Prasad

Srinivas is a Marketing professional at Softnautics working on techno-commercial write-ups, marketing research and trend analysis. He is a marketing enthusiast with 6+ years of experience belonging to diversified industries. He loves to travel and is fond of adventures.