Software Testing and Quality Assurance
Software Testing and Quality Assurance are the two most crucial steps that are more often than not neglected or underestimated. However, With newer and more advanced technologies engulfing the IT industry, it is important to devise new ways of testing and make sure that software applications remain bug-free and robust.
Machine Learning is changing the way software products and applications think and respond to queries. In the struggle to provide our machines with our intelligence, we are empowering our software to think for them and proactively learn based on experiences. And this is just what we do!
Machine Learning results in areas of image, speech, and text recognition, disease detection, human extension, error reduction, and stock prediction. It has a wide array of applications in all major industries worldwide like Healthcare, Retail, Life Sciences, Manufacturing, Enterprise, Education, Medicinal sciences, research and development, and Finance and Accounting.
Testing for ML Projects?
When we talk about testing these Machine Learning projects, we are often confused about how we can tackle them. Software Testing Services, by definition, is a fairly straightforward task. For every input, there is a definite output. We enter values into the software application, choose to make some processing, and then check the outcome. And based on the expected outcome and the actual outcome, we ensure the rightness or wrongness of a software product’s feature.
The main point here is that we already have in mind the expected output. But, Machine Learning is one scenario that does not depend on the output in a test case. Machine learning is based on neural networks, which are layered algorithms. And in this algorithm, the variables are adjusted according to the learning process. And the learning process effectively comprises using known data inputs to get the outputs which we then compare with known results. When the algorithm code reflects the known results with the desired degree of accuracy, we freeze the algebraic coefficients and generate the production code.
Main points to be taken care of by testers:
- Scenarios are the ones that need testing. That essentially means ridership, when people are willing to ride, and how much are they willing to pay. However, you need to build data models for achieving this for the expected best case, worst case, and average case.
- Don’t expect mathematical optimization of the revenue or the ridership because you are working with an algorithm that produces approximations and not Absolutely definite results.
- The Inability of the model to achieve the revenue and ridership goals is where the defects will be reflected.
It would be easy to learn it through a video, so let’s watch it.
You may follow these steps to ensure efficient testing of ML applications:
Create the ML diagnosis tree and identify the main points of your ML scenario. Then, ask yourself these-
- Do you have the right evaluation metrics?
- Do you have in mind the identification of the suitable model (linear or non-linear)?
- What is the type of your data- Sparse or Dense?
- What is the balance condition of the data?
- Identify your testing scenarios.
- Always remember to work on approximations as that's what your algorithm does.
- What are the steps taken to reduce or completely avoid over-fitting?
For a start, these steps are good to go. Machine learning applications require that the testing practices and results are changed to accommodate their differences from traditional software applications.
You can learn some Machine Learning Projects by the end of this year. The growth of AI has motivated software engineers, data scientists, and other professionals from the IT world to explore more possibilities of a career in ML. However, beginners mostly focus on theory instead of practical application. If you get success, you need to start developing ML projects sooner than later.
The tricky part is to decide where to start, and thus it is always better to take advice from experts.
Real-world instances of machine learning projects
Here, we will share some real-world instances of machine learning projects that will surely guide and make you understand what a completed project should look like.
1. Identifying tweets on Twitter (beginner)
When we talk about worldwide phenomena, hate speech and fake news come to the top on social media. Offensive posts create an issue, but the inaccurate posts through fake profiles are even worse.
NLP is a popular natural language processing application that lets us scan thousands of text documents within seconds. For instance, Twitter can process posts related to racism or sexism and related topics and separate them from others.Several stages are involved in the process:
- Tweet scraping
- Run tweets through a natural language processor
- Classify tweets with an ML algorithm
- Predict-proba method Usage for determining the probability
2. Detecting the frauds (Intermediate)
While many believe that going cashless is the best trend ever followed by people worldwide, the banking sector is under threat. The cases of credit card fraud are continuously increasing, and it is expected to soar above $32 billion by 2020.
It only takes a minute of the fraction to make fraudulent transactions every day. It brings another issue of imbalanced data.
In Machine Learning, we see fraud as a classification issue, and when the user deals with imbalanced data, the issue to be predicted is within a minority. It leads to a predictive model struggling to produce real business value using the data, which at times can go wrong.Three strategies are applied to fraud transactions:
3. Barbie with Brains (Advanced)
What if your child could have a real conversation with a doll? Modern dolls that can ‘talk’ play a key role in shaping children’s minds. The standard dolls available in the market have limited phrases and cannot conduct a real-time conversation with the child.
Hello, the Barbie project is the perfect example that demonstrates the power of ML and AI. Barbie can deliver interactive logical conversations via NLP and a few advanced audio analytics. She has a microphone on her necklace, which records the said phrases and then transmits them to the ToyTalk server.
There are three basic types of ML:
- Supervised Learning: It analyzes data history to predict new results.
- Unsupervised Learning: It searches for data patterns using statistical analysis.
- Reinforcement Learning: It works with a dynamic model that applies trial and error to boost performance levels.
Moreover, You have to grasp these applications to know how to use ML to solve your problems. However, We understand that it’s never easy to start your first ML project idea, yet you can create something great with the proper guidance of experts.