In the age of data, intelligent algorithms continue to emerge to digest and utilize this infinite resource for more innovative processes and accessible intelligence. In software development and testing, Machine Learning is unlocking data-driven pipelines, cutting time spent on repetitive tasks, saving devs labor and effort, and speeding up the release of apps.
To help you understand the total value of Machine Learning, this post comprehensively defines ML and explores the methods, models, and data behind the growing technology.
Machine Learning 101: Definition, History, and Typical Applications.
Machine Learning is a form of Artificial Intelligence, or AI. Artificial intelligence is machines' perception, synthesis, and understanding of information. Although often used interchangeably with the term, Machine Learning is a subset of AI defined as a machine's capability to demonstrate or imitate intelligent human behavior.
If we travel back in time, the origin story of AI and Machine Learning begins with Alan Turing’s 1950 paper, “Computing Machinery and Intelligence.” Turing’s paper questioned whether machines could “think” and introduced the Turing Test, which tested a machine's ability to exhibit intelligent behavior equivalent to a human.
Today, many define Machine Learning as computer systems that can learn, make decisions, and take action after model training.
Machine Learning is widely used across industries today, from software development to weather satellites to online security and fraud prevention. Machine Learning is commonly used for image and speech recognition, natural language processing, automatic language translation (or automatic captioning), medical imaging, personalized products and ad recommendations, and robotics. Machine Learning comes in many forms - from virtual assistants to chatbots to Software as a Service.
For DevOps specifically, Machine Learning reduces the time and human effort spent on repetitive, manual tasks and tests.
How Machine Learning Algorithms Conduct Tasks and Predict Outputs
Machine Learning requires an algorithm to effectively exhibit human-like intelligence, take action, and make data-driven inferences and decisions. An algorithm is a method by which the ML performs tasks by predicting the output values based on given input data. Machine Learning algorithms center on two main tasks: classification and regression.
Classification is identifying which category data belongs to based on the ML’s observation of that data after model training. For example, an ML tool that categorizes images from a traffic camera into cars running red lights vs. vehicles stopped at red lights.
Meanwhile, regression is where ML studies the relationships between independent and dependent variables to predict future outcomes. Also known as regression analysis, regression is used widely, from stock market forecasting to predicting which of your software tests are most likely to fail.
Beyond the two critical tasks of classification and regression, Machine Learning algorithms fall into three main categories: supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning is training the algorithm using labeled datasets to classify data or correctly guess outcomes. Supervised Learning is commonly used to help solve real-world problems at a larger scale and uses a training set to teach the model to deliver a desired output. Also known as Supervised Machine Learning, Supervised Learning ML is task-driven.
Unsupervised Learning uses ML algorithms to analyze and cluster unlabeled datasets, then uncover hidden patterns or data groupings. Unsupervised Learning models find similarities and differences in datasets through three main functions of clustering, association, and dimensionality reduction. Also known as Unsupervised Machine Learning, Unsupervised ML is data-driven.
Reinforcement Learning teaches a model to learn from and correct its mistakes through trial and error in an interactive environment that uses feedback from the model’s actions to teach itself. To achieve this, the algorithm is given a goal (or goals) that the agent must achieve, performable actions, and eventual feedback. Reinforcement Learning ML is teaching-driven.
Within these three kinds of Machine Learning, different models are used to perform intelligently and predict future outcomes.
The Structures and Benefits of Different Machine Learning Models
There are three significant variations of Machine Learning Models: neural networks, support vector machines, and decision trees.
Neural networks teach machines to process data and solve complex problems, as inspired by the structure and function of the human brain. Also known as deep learning, neural networks use interconnected nodes within a layered structure to create an adaptive system where models can learn from their mistakes and improve. Neural networks help ML systems make intelligent decisions with little or no human assistance.
Support vector machines are Supervised Learning models with associated learning algorithms that analyze data for classification and regression analysis. A support vector machine algorithm learns by example to assign labels to objects. Once given a set of training datasets, each is identified as belonging to one of two categories. Then, the SVM ML algorithm builds a model to assign new examples to that one category or the other.
Decision trees use classification or regression to create a predictive model to draw conclusions about the information. In classification trees, the leaves represent class labels, and the branches represent the conjunctions of features that form the class labels. Meanwhile, in regression trees, the target variable can handle continuous values.
Whatever the structure or architecture of your chosen ML model, the model will allow you to turn raw data into valuable future insights once the Machine Learning Algorithm is trained.
How Machine Learning Algorithms are Trained
To train a Machine Learning model, preparing the data is imperative. Data preparation includes transforming raw data so a machine learning algorithm can eventually learn, discover insights, and make predictions from the datasets. Data preparation involves six steps: accessing, ingesting, cleansing, formatting, combining, and then analyzing the data.
After data preparation, it’s time to train the model. The data is divided into the training dataset and the validation dataset. The training data will teach the ML model to identify patterns, learn features, and predict future outcomes. During the training, the model is run through scenarios to learn common patterns and forecast future outcomes.
After model training, model evaluation assesses the ML algorithm’s performance, strengths, and weaknesses. Model evaluation helps teams evaluate efficacy and is also essential during model maintenance and monitoring.
The Challenges Facing Machine Learning: Data and Imbalances
Machine Learning has invaluable potential within DevOps when used to help create faster, better software releases. Still, it’s important to note that ML comes with unique challenges, particularly during the model training stage.
First, there’s the issue of overfitting data, which occurs when an ML model fits too closely to the training dataset. Overfitting happens when training data does not contain enough data samples to represent all possible input data values. Overfitting negatively impacts the model's performance on new data because it's too close to the training set.
On the other hand, underfitting data is when a model fails to capture the underlying relationship of the dataset it was trained on. Underfitting happens when a model does not learn the patterns in the training data well and can’t understand new data inputs. An underfit model delivers unreliable predictions.
Then, there’s data or class imbalance, which happens when there is an unequal distribution of classes in a training dataset, and the ML algorithm assumes a data set is equally distributed. Class imbalance causes machine learning to exhibit bias towards the majority class and causes a poor classification of the minority class. If not addressed, class imbalance causes the ML model to perform poorly on minority classes, which can impact predictions.
Despite these challenges, Machine Learning is invaluable for speeding up the software testing life cycle, saving devs time, improving productivity, and increasing developer experience.
Machine Learning for Software Testing Intelligence
The software development pipeline is rich with data. With every test run, a tsunami of test suite data is available, making software testing ripe for optimization with machine learning.
Launchable’s Machine Learning model was developed to make your testing faster with smarter test selection. Our Predictive Test Selection identifies and runs the tests with the highest probability of failing, based on your code and test metadata, to drastically reduce the size of your test suite and speed up the development feedback loop.
Using previous test results and data about the changes that were tested on to train an ML model, it learns which tests passed or failed, as well as the relationships between source code changes.
With this information, the model can predict the likelihood of failure for each test based on past runs, which allows developers to only run a dynamically selected subset of tests - those most likely to fail.
By selecting the highest value and most important tests to run for a specific change, teams are able to streamline the most arduous phase within the SDLC without risking quality.
Machine learning has opened up intelligent data processing - allowing more data-driven processes and approaches to be carved out and elevating manual approaches. Software testing has been evolving with the incorporation of machine learning. We are excited about the expansion and deepening of ML models within the testing space - with more data-driven software testing, we know developer experience can be enhanced, and innovation can be further fostered.