This course aims at providing software testers with the required knowledge and skills in order to perform quality assurance tests for software applications that were developed for the Android platform. This course overviews the Android platform capabilities and provides you with up-to-date practices for performing the tests.
Incorporate Testing artifacts into each SDLC Phase and conduct SDLC Phase Test Gating for their project.
Requirements
Students do not need previous experience with the Software Development Life Cycle or Gating, but should have a basic understanding of the testing artifacts produced for a typical Software development project.
Description
The SDLC and Gating for Testing Professionals course is geared towards software testing professionals at the beginner and intermediate levels who want advance their understanding of the software development lifecycle. Test Analysts, Test Leads and Test Managers will gain an understanding of the Testing Artifacts required at each phase of the SDLC and the Gating criteria that applies to the testing activities and the testing artifacts for each phase.
The course is presented as a slide presentation with embedded video to guide you and provide additional content. The course is approximately one hour in length.
The course is divided into three main sections, each one presenting the material for each phase of the SDLC individually. The first section presents an overview of each SDLC phase and the main project activities that occur within that phase. This discussion is not limited to the testing activities, but give a more general overview of each phase. The second section gives a comprehensive look at the specific testing artifacts required at each individual phase of the SDLC. The third section presents the gating criteria for each SDLC phase as it pertains to the testing activities and testing artifacts.
This course is designed for software testing professionals as a practical guide and tool to apply to their current and future projects. The slides can be used as job aids for listing testing artifacts by SDLC phase and for gating criteria by SDLC phase.
Previous SDLC or Gating knowledge is not required. If you are a Test Lead or Test Manager who is very familiar with the SDLC and Gating, this may not be the right course for you. This course is geared towards Software Testing Professionals so may not be as useful for people outside of testing.
Who this course is for:
This SDLC and Gating course is geared towards testing professionals. Test Analysts, Test Leads and Test Managers will gain an understanding of the Testing Artifacts required at each phase of the SDLC and the Gating criteria for testing for each phase. Previous SDLC or Gating knowledge is not required. If you are a Test Lead or Test Manager who is very familiar with the SDLC and Gating, this may not be the right course for you. This course is geared towards Testing Professionals so may not be as useful for people outside of testing.
Course content
5 sections • 24 lectures • 1h 2m total lengthExpand all sections
Introduction to the Course1 lecture • 2min
Introduction01:39
SDLC and Gating Overview1 lecture • 3min
SDLC and Gating Overview02:48
SDLC Phases – Overview7 lectures • 20min
SDLC Phase – Business Requirements03:07
SDLC Phase – System Requirements04:02
SDLC Phase – Design03:27
SDLC Phase – Development03:15
SDLC Phase – Test03:29
SDLC Phase – Deploy00:55
SDLC Phase – Warranty and Project Closure01:47
SDLC Phase Activities Quiz4 questions
Test Artifacts by SDLC Phase8 lectures • 18min
Test Artifacts Introduction00:58
Test Artifacts – Business Requirements Phase01:32
Test Artifacts – System Requirements Phase02:05
Test Artifacts – Design Phase02:56
Test Artifacts – Development Phase04:05
Test Artifacts – Test Phase04:00
Test Artifacts – Deploy Phase01:22
Test Artifacts – Warranty and Project Closure Phase01:29
Test Artifacts by SDLC Phase Quiz4 questions
SDLC Phase Gating and “Go no Go”7 lectures • 19min
Ever since digitalization took center stage, various new-age technologies have come to the fore and benefited sectors in a dramatic manner. Though the emergence has not only enticed humankind to embrace software and applications to manage their day-to-day chores but pushed various organizations to adopt performance-driven solutions. According to a cybersecurity company, there are 8.93 million mobile applications today, with the Google Play Store having 3.553 million apps, the Apple App Store having 1.642 million apps, and Amazon having 483 thousand apps. Traditionally, the focus of IT organizations has been entirely on technology development; however, exposure to apps and software has enabled individuals and businesses to achieve a given goal and execute the function. In this context, performance testing and monitoring came to the rescue, allowing IT solution providers and enterprises working on business-specific solutions to help and resolve issues that could lead to a poor user experience and revenue loss.
The early phase of performance testing and monitoring methods was limited to manual procedures, but the advent of innovative technologies such as artificial intelligence (AI) and machine learning (ML) enhanced and transformed the testing and monitoring process for the better. Especially the introduction of ML (a subset of AI) has enabled computer systems to learn, identify patterns, and make predictions without being programmed. Machine learning algorithms can be trained on large datasets of performance data to automatically identify anomalies, predict performance issues, and suggest optimization strategies. According to Market Research, the global machine learning market is poised to reach INR 7632.45 billion by 2027 at a CAGR of 37.12% during the forecast period 2021-2027.
The utilization of machine learning in testing makes the process more competent and dependable. And provide several benefits, such as improved accuracy, limited test maintenance, aid in test case writing and API testing, test data generation, and reduced UI-based testing. As technology evolves, the way we develop and test also needs to change, and testing in production itself is possible when ML can show future disruptions in advance to mitigate. Testing in production means code coverage of exactly what is needed without additional spending on the test environment. Thus, ML has become a vital player in improving performance testing and monitoring, eradicating the need for creating long-winded test procedures and reducing the time spent maintaining tests.
Ways to Improve Performance Testing and Monitoring
During testing, an application may display a variety of performance issues, such as an increased latency, systems that hang, freeze, or crash, and a decrease in throughout. As a result, machine learning emerged as a solution and can be used to track the source of a problem in software. Furthermore, ML’s capabilities are useful for current concerns and anticipating future values, and comparing them to those acquired in real-time.
In addition, the critical advantage of ML algorithms is that they learn and improve over time. The model can automatically alter in reaction to data, assisting in defining what “normal” is from week to week or month to month. Not only on time series data but ML correlation algorithms can also be used to find code-level issues causing resource abuse. This means that we can consider new data patterns and generate predictions and projections that are more exact than those based on the original data pattern. So let’s delve into some of the ways in which machine learning can improve performance testing and monitoring.
Predictive Analytics: Machine learning algorithms can be trained to forecast future performance concerns based on the collected data. This can assist the organization in proactively identifying and mitigating potential performance issues before they affect users.
Automated Anomaly detection: Machine learning algorithms can learn regular application performance patterns by analyzing performance measures like response time, throughput, and resource utilization. Once trained, the algorithm can detect anomalies such as unexpected spikes or decreases in performance and alert developers and operators to the problem.
Root Cause Analysis and Optimization: Performance data can be analyzed by machine learning techniques to pinpoint the underlying causes of performance problems. This can save time and effort for developers and operators who would otherwise need to detect and fix the problem manually. Thus, it can help teams optimize resource usage and improve performance.
Correlation and Causation: ML correlation and causation techniques can identify and quantify the relationship between resources and help build a causal graph to show how they affect performance.
Real-time Monitoring: Real-time performance data analysis by machine learning algorithms can predict performance problems in advance and alert. Firms can respond to concerns more rapidly and with less impact on users.
In addition, to implement machine learning for performance testing and monitoring, businesses must gather and store vast volumes of performance data, filter data for accuracy, train machine learning models, and deploy them as needed. It is critical to highlight that machine learning is not a panacea and should be augmented with traditional performance testing and monitoring approaches to achieve the best outcomes.
Technology: Pathway to Boost Performance
In the modern era, with the growing number of software and applications, businesses are discovering that software performance at par is not just a perk for customers but a necessity. The inability to achieve the desired outcome can result in financial loss and poor customer experience that should not be overlooked. This is where the need for machine learning has become essential, which can significantly improve performance testing and monitoring by automating anomaly detection, providing predictive analytics, enabling root cause analysis, optimizing resource usage, and enabling real-time monitoring. Furthermore, as software systems become more sophisticated, machine learning will become an increasingly important tool for ensuring optimal performance and user experience.
Understand the common issues faced by companies while adopting machine learning technology.
The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028, according to a report by Fortune Business Insights. Enterprises all over the world are increasingly exploring machine learning solutions to overcome business challenges and provide insights and innovative solutions. And even though machine learning benefits are becoming more apparent, many companies are facing challenges in machine learning adoption.
As the name suggests, machine learning involves systems learning from existing data using algorithms that iteratively learn from the available data set. With this, systems are able to come up with hidden insights without being explicitly programmed where to look.
Importance Of Machine Learning
The interest in Machine Learning can be comprehended by simply understanding that there is a growth in volumes and varieties of raw data, the different processes, and hence, there is a need to find an affordable data storage.
The need of the hour is to implement a method by which organizations can quickly and automatically analyze bigger, more complex data. Not only this, by implementing and integrating Machine Learning in an organization, it becomes easier to optimize the process. How? Because Machine Learning helps deliver faster, and more accurate results.
What is simply required is to build a precise and customized model, in which Maruti Techlabs can serve as a fundamental assembling point, where your organization can find the best Machine Learning solutions.
Challenges Faced While Adopting Machine Learning
Machine learning is helping organizations make sense of their data, automate business processes, and increase productivity, and gradually profits too. And while companies are keen on adopting machine learning algorithms, they often find themselves struggling to begin the journey.
All the companies are different and their journeys are unique. But essentially, the frequently faced issues in machine learning by companies include common issues like business goals alignment, people’s mindset, and more. Let us discuss and understand the 6 most common issues which companies face during machine learning adoption.
1. Inaccessible Data and Data Security
One of the most common machine learning challenges that businesses face is the availability of data. The availability of raw data is essential for companies to implement machine learning. Data is needed in huge chunks to train machine learning algorithms. Data of a few hundred items is not sufficient to train the models and implement machine learning correctly.
However, gathering data is not the only concern. You also need to model and process the data to suit the algorithms that you’ll be using. Data security is also one of the frequently faced issues in machine learning. Once a company has dugged up the data, security is a very prominent aspect that needs to be taken care of. Differentiating between sensitive and insensitive data is essential to implementing machine learning correctly and efficiently.
Companies need to store sensitive data by encrypting such data and storing it in other servers or a place where the data is fully secured. Less confidential data can be made accessible to trusted team members.
2. Infrastructure Requirements for Testing & Experimentation
Most companies that are facing machine learning challenges have something in common among themselves. They lack the proper infrastructure which is essential for data modeling and reusability. Proper infrastructure aids the testing of different tools. Frequent tests should also be allowed to develop the best possible and desired outcomes, which in turn, assist in creating better, stout, and manageable results.
Companies that lack the infrastructure requirements can consult with different firms to model their data groups aptly. Then, they can compare the results with a different perspective and the best one can be adopted accordingly by the company and subsequently, by the board.
The stratification method is usually used to test machine learning algorithms. In this method, we draw a random sample from the dataset which is a representation of the true population. The common practice is to divide the dataset in a stratified fashion. Stratification simply means that we randomly split the dataset so that each class is correctly represented in the resulting subsets — the training and the test set.
3. Rigid Business Models
Machine learning requires a business to be agile in their policies. Implementing machine learning efficiently requires one to be flexible with their infrastructure, their mindset, and also requires proper and relevant skill sets.
However, implementing machine learning doesn’t guarantee success. Experimentations need to be done if one idea is not working. For this, agile and flexible business processes are crucial. Flexibility and rapid experimentations are the solution to rigid monoliths.
If one of the machine learning strategies doesn’t work, it enables the company to learn what is required and consequently guides them in building a new and robust machine learning design. The willingness to adapt to failures and learn from them greatly increases the company’s chances of successful machine learning adoption.
4. Lack of Talent
This is the most worrying challenge faced by businesses in machine learning adoption. While the number of machine learning enthusiasts has increased in the market, it’ll still take a while for the same numbers to reflect on the number of machine learning experts.
With artificial intelligence and machine learning being relatively younger technologies in the IT industry, the talent pool required to fully understand and implement complex machine learning algorithms is limited. And if you don’t have the right people to implement it, then it is difficult to unlock the true potential of machine learning applications.
Organizations are gradually realizing the avenues machine learning can open up for them. As a result, the demand for experienced data scientists has skyrocketed. And so have the salaries in this space. Job sites list data scientists as one of the highest paying jobs of 2020. With more and more organizations getting on board with big data, AI and ML, this demand is only going to increase in the coming years.
One path companies are taking to overcome this challenge is collaboration. Organizations are partnering up with companies that have the skillset and the experience to harness the power of machine learning and implement the offerings to suit your organization’s business goals.
5. Time-Consuming Implementation
Patience goes a long way in ensuring that your efforts bear fruits. And this cannot be truer for machine learning. One of the most common machine learning challenges is impatience. Businesses that implement machine learning usually expect it to magically solve all their problems and start bringing in profits from the get-go.
Implementing machine learning is a lot more complicated than traditional software development. A machine learning project is usually full of uncertainties. It involves gathering data, processing the data to train the algorithms, engineering the algorithms, and training them to learn from the data which suits your business goals.
It involves a lot of intricate planning and detailed execution. And yet, due to multiple layers and the usual uncertainties regarding the behavior of the algorithms, it is not guaranteed that the time estimated by your team for machine learning project completion will be accurate. Therefore, it is very important to have patience and an experimentative approach while working on machine learning projects. To achieve desirable results on adoption machine learning, you should give your project and your team plenty of time.
6. Affordability
If you’re looking to adopt machine learning, you will require Data Engineers, a Project Manager with a sound technical background. In essence, a full data science team isn’t something newer companies or start-ups can afford.
As a result, employing a machine learning method can be extremely tedious, but can also serve as a revenue charger for a company. However, this is only possible by implementing machine learning in newer and more innovative ways. Adopting machine learning is only beneficial if there are different plans, so regardless of one plan not performing up to the desired standards, the other can be put into action. Getting a glimpse into which machine learning algorithm would suit an organization is the only issue that one needs to get by. Once you get the best algorithm with which you’re achieving the required outcomes, you shouldn’t stop experimenting and trying to find better and more innovative algorithms.
Budgeting as per different milestones in the journey works out well to suit the affordability of the organization. If you are not confident on the talent required to implement a full-fledged machine learning algorithm, you can always go for a consultation with companies that have the expertise and experience in machine learning projects.
As a machine learning solutions provider, we at Maruti Techlabs, help you reap the benefits of machine learning in line with your business goals. Our machine learning experts have worked with organizations worldwide to provide machine learning solutions that enable rapid decision making, increased productivity, and business process automation.
