Menu

Principal Component Analysis and Dimension Reduction – Part II

Category Data Engineering

In the previous post, we saw why we should be interested in Principal Component Analysis. In this post, we will do a deep dive and get to know how this is implemented.

Now that you have some idea about how to change higher dimensions to lower dimensions, we will go through the below description which is shown in a jupyter notebook.

I have downloaded the data of three companies that are in the Indian stock market from Quandl. We will try to understand the Indian ecosystem using this.

These data are for the Hitech Corporation Ltd. Housing and Development Corporation Ltd. and Bhagyanagar India limited. The features of the data set are Date, Open pricing, the maximum price reached during the day, minimum price during the day, last price before closing, closing price, total traded quantity and turn over in lakhs.

In order to load the data set, we will use the pandas library. If you are interested, in the dataset, you can create an account on the website which is free and download the data sets.

 import pandas as pd
 df_hitech = pd.read_csv("NSE-HITECHCORP.csv")
 df_hitech.head()
view rawpca1.py hosted with ❤ by GitHub

 df_hudco = pd.read_csv("NSE-HUDCO.csv")
 df_hudco.head()
view rawpca2.py hosted with ❤ by GitHub

Taking a look at the shape of the matrices.

 print(df_hitech.shape, df_bhagyanar.shape, df_hudco.shape)
view rawpca3.py hosted with ❤ by GitHub

((51, 8), (55, 8), (53, 8))

So our three data sets are three matrices of dimensions (48 x 8), (52 x 8) and (50 x 8) where the columns are the different features and every row represents a separate data sample.

Visualization

To get the feeling of how the different stocks are distributed along opening stock prices and total trade quantities, let us visualize them using histograms.
Get the relevant data. Right now we are only interested in visualization and as we cannot show all the features in a plot, we will just focus on some specific columns.

 import numpy as np
 from bokeh.charts import output_file, show, Line
 from bokeh.charts import Area, show, TimeSeries
 from bokeh.io import output_notebook
  
 X1 = df_hitech.loc[0:5, ['Date', 'Open', 'Total Trade Quantity']].values
 X2 = df_bhagyanar.loc[0:5,['Date', 'Open', 'Total Trade Quantity']].values
 X3 = df_hudco.loc[0:5, ['Date', 'Open', 'Total Trade Quantity']].values
  
 # Get a feel for the data.
 print([x for x in X1])
 print([x for x in X2])
view rawpca4.py hosted with ❤ by GitHub

[array([‘2017-08-02’, 209.1, 2935.0], dtype=object), array([‘2017-08-01’, 212.0, 5094.0], dtype=object), array([‘2017-07-31’, 212.0, 6803.0], dtype=object), array([‘2017-07-28’, 208.0, 2023.0], dtype=object), array([‘2017-07-27’, 213.05, 6714.0], dtype=object), array([‘2017-07-26’, 218.95, 3389.0], dtype=object)]
[array([‘2017-08-02’, 24.5, 5857.0], dtype=object), array([‘2017-08-01’, 24.75, 12622.0], dtype=object), array([‘2017-07-31’, 25.05, 31276.0], dtype=object), array([‘2017-07-28’, 25.15, 11483.0], dtype=object), array([‘2017-07-27’, 26.0, 19290.0], dtype=object), array([‘2017-07-26’, 26.5, 17808.0], dtype=object)]

Show how the data is distributed for one company.

 data = dict(
 hitech=[x[1] for x in X1],
 bhagyagar=[x[1] for x in X2],
 date=[x[0] for x in X1],
 )
  
 line_plot = Line(data, x='date', title="Area Chart", legend="top_left",
 xlabel='time', ylabel='memory')
 show(line_plot)
view rawpca5.py hosted with ❤ by GitHub

 

Standardizing
We want to compare oranges to oranges and apples to apples. Generally, we are more interested in how the changes are happening and not too much concerned about the scale on which the data set was published. In this case, you can see the data set is comprised of numbers that were gathered on various scales, so it would be wise to standardize the data set to the unit scale with mean as 0 and variance as 1.

 from sklearn.preprocessing import StandardScaler
  
 column_names = ['Open', 'High', 'Low', 'Last', 'Close', 'Total Trade Quantity', 'Turnover (Lacs)']
  
 X_hitech = df_hitech.loc[:, column_names].values
 X_bhagyanagar = df_bhagyanar.loc[:, column_names].values
 X_hudco = df_hudco.loc[:, column_names].values
  
 X = np.concatenate([X_hitech, X_bhagyanagar, X_hudco], axis=0)
  
 X_std = StandardScaler().fit_transform(X)
  
 print(X_std.shape)
view rawpca6.py hosted with ❤ by GitHub

(159, 7)

Covariance matrix
We can perform the Eigen decomposition on the covariance matrix.
In terms of code, this can be represented using the below format.
Please note that the shape of the standardized matrix is 150 x 7. So we should get a covariance matrix of 7 x 7.

 mean_vec = np.mean(X_std, axis=0)
  
 cov_mat = (X_std - mean_vec).T.dot((X_std - mean_vec)) / (X_std.shape[0]-1)
  
 print('Covariance matrix \n{}'.format(cov_mat))
 print('Covariance matrix shape is: {}'.format(cov_mat.shape))
view rawpca7.py hosted with ❤ by GitHub

Covariance matrix
[[ 1.00632911 1.00550536 1.00597911 1.00542205 1.00545316 -0.06315605
-0.05901863]
[ 1.00550536 1.00632911 1.00541311 1.00598897 1.00602285 -0.0542054
-0.0497978 ]
[ 1.00597911 1.00541311 1.00632911 1.00562847 1.00565687 -0.06570958
-0.06198619]
[ 1.00542205 1.00598897 1.00562847 1.00632911 1.00628689 -0.06051605
-0.05646143]
[ 1.00545316 1.00602285 1.00565687 1.00628689 1.00632911 -0.05997139
-0.05587568]
[-0.06315605 -0.0542054 -0.06570958 -0.06051605 -0.05997139 1.00632911
0.99963087]
[-0.05901863 -0.0497978 -0.06198619 -0.05646143 -0.05587568 0.99963087

1.00632911]]
Covariance matrix shape is: (7, 7)

We showed the code just to show how the covariance works. In general, the cov function is used.

 covariance_matrix = np.cov(X_std.T)
 print(covariance_matrix)
view rawpca8.py hosted with ❤ by GitHub

[[ 1.00632911 1.00550536 1.00597911 1.00542205 1.00545316 -0.06315605
-0.05901863]
[ 1.00550536 1.00632911 1.00541311 1.00598897 1.00602285 -0.0542054
-0.0497978 ]
[ 1.00597911 1.00541311 1.00632911 1.00562847 1.00565687 -0.06570958
-0.06198619]
[ 1.00542205 1.00598897 1.00562847 1.00632911 1.00628689 -0.06051605
-0.05646143]
[ 1.00545316 1.00602285 1.00565687 1.00628689 1.00632911 -0.05997139
-0.05587568]
[-0.06315605 -0.0542054 -0.06570958 -0.06051605 -0.05997139 1.00632911
0.99963087]
[-0.05901863 -0.0497978 -0.06198619 -0.05646143 -0.05587568 0.99963087
1.00632911]]

Next is to perform an eigen decomposition on the above matrix. Eigen decomposition is the method where we decompose a square matrix into its Eigen vectors and Eigen values. The Eigen vectors are basically hyperplanes to which the data gets projected into. Take a look at wikipedia to learn more.

We find the Eigen vectors and the Eigen values using the numpy library. In PCA the Eigen vectors are called loadings. Let’s call them the matrix W.

 eigen_vals, eigen_vecs = np.linalg.eig(covariance_matrix)
  
 print(eigen_vals)
 print(eigen_vecs)
view rawpca9.py hosted with ❤ by GitHub

[ 5.04062300e+00 1.99469506e+00 6.70989466e-03 1.52221819e-03
5.08344416e-04 2.04592597e-04 4.06913481e-05]
[[-0.44636953 -0.0255697 -0.00233638 0.61091357 0.39241169 -0.52200819
0.02000256]
[-0.44626454 -0.03201096 -0.03801748 -0.31356635 0.66065443 0.51116882
-0.04791904]
[-0.44644488 -0.02362002 0.04386902 0.47716468 -0.50791923 0.55804311
-0.03353175]
[-0.44641063 -0.02742468 0.0018537 -0.3959805 -0.28922772 -0.3236239
-0.67437107]
[-0.44640923 -0.02782594 -0.0024592 -0.37808739 -0.25605417 -0.22367243
0.73579569]
[ 0.04405545 -0.70563431 0.70623273 -0.01919751 0.03123937 -0.00518154
0.0018351 ]
[ 0.04224484 -0.705916 -0.70558526 0.02560183 -0.03707468 0.00362514
-0.00206853]]

To know why the Eigen values and Eigen vectors are important, take a look at the math below. If this sounds too heady, you can skip this section and just focus on the code.
So, we know that after performing the Eigen decomposition we get the Eigen values and the Eigen vectors. Let’s call them λ and W. Therefore, in terms of principal component analysis, we will say that the scores are the product of matrices X and W, i.e.,

assuming T is the score.

Singular Value Decomposition
Finding the Eigen values and Eigen vectors are computationally not the most efficient way of finding the loadings or the matrix W. We have a shortcut to do the same thing and that is through Singular Value Decomposition or SVD.
How it works is that the same input matrix X can be broken down to three matrices U, Σ, and V*.

Let us call this equation 1.
U is the left singular vector and V* is the right singular vector. So Σ will contain singular values in its diagonal and they are ordered. Please note that although V* means conjugate of V. Since we will be dealing with real world data, you can think of this as the transpose of V.
Now, interestingly the matrix V turns out to be identical to the loadings or W.
How is this transformation beneficial to us. Let’s explore that below. We know that the scores T can be shown with the given equation

Coming from equation 1 above we can write –

Since V*V is the Identity Matrix, and W is the same as V, we get

So we can find the scores if we can compute U and Σ, which can be easily done. If you are interested in trying this right away, take a look at this SVD computation example.

Selecting Principal Components

Now we will select the principal components. This is mostly based on human judgment but many thumb rules can be followed. Understanding of how many components you incorporate will ultimately come through experience and experimentation.

Right now with the help of the Eigen decomposition we transformed our data to the new set of planes with the eigen vectors as the axes.

We need to decide which Eigen vectors can be dropped without losing too much information. Please note that the Eigen values with the least amount of value are the ones that have the least amount of information regarding the data distribution. Why is that so? Remember the definition of Eigen vectors and Eigen values.

The eigen value λ is the amount the vectors elongate or shrink. So, if the value of λ is small that would mean that the effect ν has on matrix A will be small.
We will try to see how much each value contributes to the overall variance.

We will try to see how much each value contributes to the overall variance.

 total = sum(eigen_vals)
 distribution = [100 * (i/total) for i in sorted(eigen_vals, reverse=True)]
  
 cumulative_distribution = []
 sum = 0
 for i in distribution:
 sum += i
 cumulative_distribution.append(sum)
  
 bottom = [0] + cumulative_distribution[:-1]
 left = [2*x for x in range(7)]
 right = map(lambda x: x+1, left)
 print(cumulative_distribution)
 print(len(cumulative_distribution))
 print(bottom)
 print(len(bottom))
 print(list(left))
 print(list(right))
view rawpca12.py hosted with ❤ by GitHub

[71.556013843924504, 99.872439612636256, 99.967692385037282, 99.989301591992117, 99.996517981727536, 99.999422351033218, 99.999999999999986]
7
[0, 71.556013843924504, 99.872439612636256, 99.967692385037282, 99.989301591992117, 99.996517981727536, 99.999422351033218]
7
[0, 2, 4, 6, 8, 10, 12]
[1, 3, 5, 7, 9, 11, 13]

 from bokeh.plotting import figure, show
  
 p = figure(width=400, height=400)
 p.quad(top=cumulative_distribution,
 bottom=bottom,
 left=[1, 2, 3, 4, 5, 6, 7],
 right=[1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2],
 color="#B3DE69")
  
 show(p)
view rawpca13.py hosted with ❤ by GitHub

You can see that after the first two components the contribution of the remaining to the total is almost nonexistent and cannot be seen at all. They are there and you can zoom in to see the others. The first two principal components can explain more than 99% of the data that we have.

Now, since you know where the majority of the data in your data set is coming from it becomes obvious to trim those extra dimensions. Effectively we will construct a projection matrix to transform our data to a new feature subspace. In this case, we will trim all the other features and take into account only two features. So we are reducing our 7-dimensional feature space to 2-dimensional feature subspace. This is possible since we are only choosing the top 2 Eigen vectors with the highest Eigen values to construct our (150 x 2) Eigen vector matrix W.

 # Mapping eigen value to eigen vector
 eigen_pairs = [(np.abs(eigen_vals[i]), eigen_vecs[:,i]) for i in range(len(eigen_vals))]
  
 # Sort the (eigenvalue, eigenvector) tuples from high to low
 eigen_pairs.sort()
 eigen_pairs.reverse()
  
 # check if the value is decreasing
 print([e[0] for e in eigen_pairs])
view rawpca14.py hosted with ❤ by GitHub

[5.0406230005245538, 1.9946950557326693, 0.0067098946634397017, 0.0015222181861223813, 0.00050834441617230038, 0.00020459259729253709, 4.0691348102287684e-05]

https://gist.github.com/infinite-Joy/91177e07b9916e2f268e33a4154c6360/

[[-0.44636953 -0.0255697 ]
[-0.44626454 -0.03201096]
[-0.44644488 -0.02362002]
[-0.44641063 -0.02742468]
[-0.44640923 -0.02782594]
[ 0.04405545 -0.70563431]
[ 0.04224484 -0.705916 ]]

Dimensionality Reduction

Using this new loading matrix W, we can reduce the dimensions of the original matrix X. This can be done using the matrix multiplication property, whereby if you multiply two matrices of dimensions (m x n) and (n x p), you get a new matrix of dimensions (m x p). So, in this case, we multiply matrix X, which is (159 x 7) and matrix W, which is (7 x 2) to get a new matrix (159 x 2).

Taking a look at the code below.

 Y = X_std.dot(matrix_w)
  
 print(Y[:4])
view rawpca16.py hosted with ❤ by GitHub

array([[-3.12570525, 0.1778062 ],
[-3.22460186, 0.17146108],
[-3.26460636, 0.16888734],
[-3.227314 , 0.17157395]])

If you remember, the shapes of hitech, bhagyanagar and hudco were (51, 8), (55, 8), (53, 8). Hence, to create the mappings, we create a Z vector/list with shape (159, 1).

https://gist.github.com/infinite-Joy/infinite-Joy/446130b2e02e914359ecca1e5610916d

You can see that two companies are very tightly packed, saying that there is not much variation in them while the third company has a lot of variation. This company has had a rocky year.

In production
The code that is shown above is mainly for learning purposes. In production, we will be using the inbuilt function from the sklearn library.

 from sklearn.decomposition import PCA as productionPCA
 prod_pca = productionPCA(n_components=2)
 Y_prod = prod_pca.fit_transform(X_std)
  
 print(Y_prod[:2])
  
 print(Y[:2])
view rawpca18.py hosted with ❤ by GitHub

The graphs are opposite as the matrices are reversed in their sign. In case you are implementing this in production, you may not be finding the eigen values to understand if you are capturing the majority of the variance in the data set, as was done earlier. So, to find if your new feature subspace is capturing the intended amount of variance, you can use the explained_variance_ratio_ attribute.

 print(prod_pca.explained_variance_)
 print(prod_pca.explained_variance_ratio_)
 print(np.sum(prod_pca.explained_variance_ratio_.tolist()))
view rawpca20.py hosted with ❤ by GitHub

[ 5.00892097 1.9821498 ]
[ 0.71556014 0.28316426]
0.99872439612636232

Generally, people see the explained variance ratios or keep a hard cutoff threshold of maybe 95%. In our case, the cumulative graph, generated by pca13.py was getting most of the variation and the inflection shoulder was quite steep. But even in cases where the shoulder is quite shallow, having such a cutoff will enable you to have a principled and consistent way to understand your data.

You can also download the whole blog as a jupyter notebook if you want to try out the code yourself.

Now that you know about Principal Component Analysis take a look at the decision systems around you, where data is the primary input and try to observe if they are using more data than necessary. If not, there is your gold mine.

 

References:
Siral Raval. Dimensionality Reduction. YouTube

Importance of Principal Component Analysis. Quora

Principal Component Analysis.Washington University.YouTube

Minimizing reconstruction error is equivalent to maximizing variance.Statistics StackExchange

Making sense of Eigen Vectors and Eigen Values and PCA – stats.stackexchange.com

plotly.Principal Component Analysis.

Recommended Reading

Explore our insightful articles, whitepapers, and case studies that delve deeper into the latest industry trends, best practices, and success stories. Gain valuable knowledge and stay informed about the ever-evolving landscape of digital transformation.

Navigating The Two Major Data Trends in 2024

As the data landscape continues to evolve rapidly, businesses are compelled to stay abreast of emerging trends to maintain competitiveness. In the year 2024, two prominent trends are poised to redefine data analytics: the proliferation of Generative AI and the adoption of modern data contracts. These trends not only reshape how organizations utilize data but also underscore the importance of ethical considerations and robust governance in data management. This article explores these trends in-depth, providing insights into effective strategies for implementation and the implications for businesses navigating the data landscape.Trend #1: The Ascendancy of Generative AIGenerative AI, characterized by its ability to create new content autonomously, has gained significant traction across industries. The advent of large language models (LLMs) has propelled Generative AI into the mainstream, with tech giants like Microsoft, Google, and Meta integrating Generative AI capabilities into their products. As businesses increasingly rely on AI-driven insights, Generative AI is poised to become an indispensable tool for enhancing productivity and driving innovation.Strategy for Effective Implementation:To leverage Generative AI effectively, businesses must develop a comprehensive strategy tailored to their specific needs and objectives. This strategy should encompass several key components:Identifying suitable use cases:Organizations should identify areas where Generative AI can augment existing processes and generate tangible value. Whether it’s automating content creation, personalizing customer experiences, employee training, or optimizing business operations, identifying the right use cases is essential for maximizing ROI.Comprehensive employee training:Implementing Generative AI requires upskilling employees to ensure they can effectively utilize AI tools while adhering to ethical guidelines and best practices. Training programs should cover topics such as data privacy, bias mitigation, and ethical AI usage to foster a culture of responsible AI adoption.Strong data governance:Robust data governance is critical for ensuring the accuracy, security, and ethical usage of AI-generated insights. Organizations must establish clear guidelines and protocols for data collection, storage, and usage to mitigate risks associated with data misuse or bias.Managing costs and licensing:While Generative AI offers immense potential, it also comes with significant costs, both in terms of technology investments and licensing fees. Organizations must develop a cost-effective strategy for scaling AI initiatives while ensuring compliance with budgetary constraints.Balancing automation and human judgment:While AI-driven insights can enhance decision-making processes, it’s essential to strike a balance between automation and human judgment. Human oversight is crucial for interpreting AI-generated insights, identifying biases, and ensuring ethical decision-making.Ethical considerations:As AI becomes increasingly integrated into business operations, organizations must prioritize ethical considerations and accountability. This includes addressing issues related to data privacy, algorithmic bias, and the potential societal impact of AI-driven decisions.Trend #2: Adoption of Modern Data ContractsModern data contracts have emerged as a solution to streamline data usage and sharing, effectively addressing the challenges associated with broken data integrations and communication gaps between application and analytics teams.Structured Data Interactions:Modern data contracts represent a paradigm shift in how organizations manage data interactions. Unlike traditional contracts, which are static and cumbersome to maintain, modern data contracts are dynamic agreements that evolve with changing data requirements and business needs.Integration into workflows:By integrating data contracts into existing workflows and development processes, organizations can ensure seamless data interactions across disparate systems and applications. This integration enables teams to collaborate more effectively, reducing friction and improving data quality and consistency.Implementation Strategies:Implementing modern data contracts requires a strategic approach focused on collaboration, standardization, and automation. Key strategies include:Developing clear standards:Organizations should establish clear standards and guidelines for data contracts, outlining key parameters such as data formats, schemas, and validation rules. These standards help ensure consistency and interoperability across data systems and applications.Instituting change controls:Change management processes are essential for managing versioning and ensuring smooth transitions between data contract iterations. By implementing robust change controls, organizations can minimize disruptions and maintain data integrity throughout the contract lifecycle.Training and tools:Equipping teams with the necessary training and tools is crucial for successful data contract implementation. Training programs should cover topics such as contract management, data governance, and compliance, while tools such as data modeling platforms and contract management software can streamline the contract development and deployment process.As businesses navigate the complexities of the data landscape in 2024, adapting to the rise of Generative AI and modern data contracts is essential for driving innovation and maintaining competitiveness. By developing comprehensive strategies for AI adoption and data governance, organizations can harness the transformative power of Generative AI while ensuring ethical and responsible data usage. Likewise, embracing modern data contracts enables organizations to streamline data interactions, improve collaboration, and enhance data quality and consistency. By embracing these trends and implementing best practices, businesses can unlock new opportunities for growth and success in the digital age.

Learn More >

The Journey from System Admin to DevOps Superstar

According to a report by the Economic Times, when organizations cultivate a better work environment, the overall experience improves exponentially. They find true meaning in their jobs by prioritizing employees’ mettle, exceeding expectations, and work allocation.Employees seek exposure and opportunities in their jobs. By building productivity and customer satisfaction they enhance their portfolio.Radhakrishnan one of our DevOps superstars, has contributed with his service and time for over 8 years. To commemorate this everlasting relationship we got into a candid conversation with him. Here’s what he had to say about his journey before and with Nineleaps.Radhakrishnan is originally from a small town near Bengaluru, Hosur. After completing his MBA, his interest developed in computers and networking. He successfully gained appropriate knowledge by undertaking network courses and embarked on a journey to becoming a system admin. He enjoyed working for various companies as a system admin.Then came Nineleaps which gave new horizons of opportunities to his mettle. When we asked him about his transition from a system admin to a DevOps engineer, he fondly remembered a quote given to him by our CEO on the day of his selection.“You are on the flight now, just fly,” — Divy Shrivastava.And, so he did.Divy’s words of confidence boosted his resolve. The walk towards DevOps became a sprint, as multiple iterations of knowledge and experience suffused him. The arena of his work leaped and much to his admiration, he realized DevOps to be his passion and soul.Right from the get-go, an intensive training regimen, honing his skills, immersing himself in countless hours of study, and shadowing esteemed senior members of our organization he grasped the crucial importance of comprehending tasks and prioritizing them effectively. Driven by an unwavering desire to learn and prove his mettle, his transition from a system admin to a DevOps maestro was seamless. Multiple training sessions helped him get a deeper understanding of internal and external projects as well as the product, giving him never-to-dull confidence.Learning and development, knowledge transfers, and peer learning are certainly at the core of Nineleaps which helped him become the super engineer he is today. These trainings were both from the client’s side as well as in-house learning at Nineleaps.“In my opinion what sets Nineleaps apart is our dynamic and flexible approach to projects, with extensive focus on Agile methodology we are trained and nourished to build quality solutions for our clients, and also are facilitated with high-tech exposure by working with industry giants and rewarded with the utmost respect and growth opportunities.”To understand more closely we asked him about the challenges he faced at times, and according to him, documentation was a challenge. He feels all the work that the employee is doing must be documented and organized in a proper way as it will help them in the future. He also informed about instances where a person working on a specific problem might face similar challenges later in the same week and not be able to recall what the solution was properly, in such cases documenting everything became important. The organization’s culture was very open and asking questions or requesting help was never an issue which facilitated collaboration in resolving such challenges.Nineleaps became the crucible to test his mettle and with each strike of the hammer, a superstar was born.

Learn More >

Performance Testing Trends: Future of Software Optimization

Performance testing is an integral part of the software development lifecycle as it helps determine the scalability, stability, speed, and responsiveness of an application as compared to the workload given. It is not a standalone process and should be run throughout the software development process.It serves the purpose of assessing various aspects of an application’s performance, such as application output, processing speed, data transfer velocity, network bandwidth usage, maximum concurrent users, memory utilization, workload efficiency, and command response times. By evaluating these metrics, performance testers can gain valuable insights into the application’s capabilities and identify any areas that require improvementUsing AI to automate testing:Performance testing encompasses various stages, each posing unique challenges throughout the testing lifecycle. These challenges include test preparation, execution, identifying performance bottlenecks, pinpointing root causes, and implementing effective solutions. AI can help reduce or even eliminate these differences. AI-powered systems can handle the mountains of data collected during performance testing and be able to produce efficient and accurate analyses. AI can also identify the sources of performance slowdowns in complex systems, which can otherwise be tedious to pinpoint. With AI-driven automation, performance testers can streamline the testing process, ultimately saving time and resources while ensuring reliable results.Open Architecture:Performance testing, which evaluates how well a system performs, is undergoing a significant shift away from relying solely on browser-based evaluations. Instead, internet protocols like TCP/IP are being adopted for comprehensive performance monitoring. This approach emphasizes the need for system components to work together harmoniously while assessing their performance individually. The integration of cloud-based environments has become crucial, as cloud computing is an integral part of modern technology infrastructure. Cloud-based environments provide a flexible and reliable platform that enables seamless integration and coordination of various components, ultimately leading to enhanced system performance. It is crucial to prioritize comprehensive performance testing, which involves evaluating individual component performance, managing loads, monitoring in real-time, and debugging, to ensure optimal system performance.Self Service:When adopting the aforementioned trends, it’s essential to consider practical implementation tips for successful outcomes. For instance, performance engineers can use AI-powered tools to analyze performance data more effectively, leading to more accurate and actionable insights. Integrating cloud-based solutions can provide the flexibility and scalability required for modern performance testing demands. As stakeholders implement these trends, the collaboration between development, testing, and IT operations teams becomes crucial for successful integration and improved application performance.SaaS-based Tools:Testers can now easily set up and execute tests at cloud scale within minutes, thanks to the convergence of self-service, cloud-based testing, SaaS, and open architecture. Unlike older desktop-based tools that demand extensive setup, the emerging tools simplify the process with just a few clicks. Furthermore, these modern technologies offer seamless interoperability, significantly enhancing performance capabilities.Changing Requirements:In classic app testing, testers had to make educated guesses about the software’s use and create requirements and service-level agreements accordingly. However, in DevOps-oriented environments, performance requirements are seen as dynamic and evolving. Traditional requirements are now driven by complex use cases, accommodating different user experiences across various devices and locations. Performance engineering plays a critical role in continuously monitoring systems and proactively identifying and resolving issues before they can negatively impact customer retention or sales.Sentiment analysis:Monitoring production provides insight into server response times but does not capture the true customer experience. Synthetic transactions, on the other hand, simulate real user actions in production continuously. They can range from basic interactions like logging into an e-commerce site and adding products to a cart, to more complex transactions that track performance end to end without actually completing real orders or charging credit cards. Tracking the actual user experience is crucial for identifying bottlenecks, delays, and errors in real-time, as some issues may go unreported by users. Sentiment analysis is a powerful technology that evaluates customer responses based on emotions, providing valuable insights from customers’ reactions expressed in plain text and assigning numerical sentiment scores.Chaos Testing:Chaos testing is a disciplined methodology that proactively simulates and identifies failures in a system to prevent unplanned downtime and ensure a positive user experience. By understanding how the application responds to failures in various parts of the architecture, chaos testing helps uncover uncertainties in the production environment. The main objective is to assess the system’s behavior in the event of failures and identify potential issues. For instance, if one web service experiences downtime, chaos testing ensures that the entire infrastructure does not collapse. This approach helps identify system weaknesses and addresses them before reaching the production stage.Conclusion:As software development continues to evolve, performance testing must keep pace with emerging trends and technologies. By leveraging AI-driven automation, open architecture with cloud integration, and practical implementation tips, stakeholders can optimize their performance testing processes to deliver high-performing and responsive software applications. Real-world examples and a focus on key performance metrics ensure that these trends are not only understood but effectively implemented to achieve the desired outcomes. Embracing these trends empowers software development teams to elevate the user experience, enhance customer satisfaction, and drive business success.

Learn More >

Ready to embark on a transformative journey? Connect with our experts and fuel your growth today!

Explore Now