6 Key Ideas Every Developer Should Know
Statistics might seem like a distant concept from software engineering, yet its influence is remarkable. Lets dive into six key ideas you need to know to stay relevant in the long run.
1) Overparameterization
A.K.A. massive Neural Networks
Google’s Jeff Dean mentioned that 500 lines of Machine Learning code replaced 500,000 lines of code in Google Translate. A 1000x reduction.
Your Competition
As a developer, if you see your role as creating algorithms, Machine learning models are not just tools; they’re your competition.
Regardless of the accepted Leetcode solutions to search problems, the core of major search engines isn’t traditional algorithms like binary search or depth-first search, but machine learning models because these models approximate vastly more capable functions than binary search or depth-first search.
But there’s a silver lining: these models still need human work. Developers are essential for writing the 500 lines of code that powers them and for preparing the data they learn from.
What are Neural Networks
Neural networks, inspired by the human brain’s ability to recognize patterns, consist of layers of interconnected nodes (neurons), each processing a part of the input. The strength of a neural network lies in its parameters — weights and biases, which are refined during training. Networks with more parameters are generally more powerful, making them suitable for complex tasks like image recognition, natural language processing, and user behavior analysis in software.
Overparameterization is key
In neural networks, overparameterization means having more parameters than training data points. Surprisingly, this doesn’t invariably lead to overfitting, where a model performs well on training data but poorly on new data. Larger networks with more parameters can generalize better, capturing complex patterns in high-dimensional data more effectively.
Practical Application:
In your next coding interview, if you’re asked to determine the best buying and selling points for a stock. Instead of resorting to dynamic programming, consider training a neural network to find a better solution 😉
2) Counterfactual Causal Inference
Simulate the problem before deciding the solution
Understanding Cause and Effect
At the heart of many decisions in software development lies the question of causality: “Does changing X cause Y?”
Imagine your company wants to enhance customer support. In one world, your product team immediately gets to work implementing a chatbot based solution, in another they choose to work on another feature. Which decision would be correct?
Counterfactual Causal Inference can help your product team predict the future; by guessing the chatbot’s impact on customer satisfaction without the need to build it, making you more sure about your decisions.
Here is how:
1, Develop a predictive model
Collect past customer support data; such as response times, resolution rates, number of queries, etc. Build a machine learning model to make good predictions based on what happened.
2, Define the Goal
Find out which changes are desired; like faster response times and higher resolution rates.
3, Define the impact
Make assumptions about how the chatbot could improve these.
4, What really moves the needle?
Think of the model as window into the future and run the simulation multiple times with different assumptions about the chatbot’s impact (e.g., varying degrees of response time reduction). See how your guesses affect customer satisfaction and use this to identify ot the right variables to change.
5, Decide
Armed with information about which variables most matter and which don’t your team can decide if building the chatbot is worth it.
Lastly, Test
If you decide to build it, Pilot test the chatbot in a controlled environment to see if the real-world effects validate if your model’s guesses. If they do, it’s highly likely that your product team has made the right choice. If they don’t, be wary of the model’s effectiveness as a window into the future.
Practical Implications
Using this method changes how software teams plan work. Before designing a solution, they have to carefully describe and use statistics to model the problem. But the benefit is clear for those who choose to adopt it because they will have far greater clarity about what it is they are trying to achieve and what matters in order to get there
3) The Bootstrap Method
Reliable Software Performance Analysis
The bootstrap method provides a way to understand how unsure you are. It might seem odd to want to know the answer but uncertainty is at the heart of all difficult decisions.
The Method in Action
Imagine you’re part of a growing startup that recently rolled out a new feature. There’s a big client demo scheduled tomorrow and you’ve gathered a month’s worth of system response times for similar sized customers. You want to make sure the system is snappy and so you check important measures, like the 95th percentile response times (p95), but you’re worried. Can you really rely on this, given its based on just a month’s data? What if the system doesn’t perform well during the crucial demo?
What to do?
You can bootstrap a 1,000 new datasets from your original month of data. For each sample, you recalculate the p95 and discover that across these samples it varies between 1.2 to 2.1 seconds, this might be acceptable. With this insight, you can approach tomorrow’s demo with more confidence, because you have a better understanding of how unsure you are.
4) Multilevel Models
Integrating Complexity and Nuance into Software Analytics with Multilevel Models
In a Nutshell
When a developer analyzes user interaction data from an application used across various platforms (like mobile, desktop, web), this data isn’t merely a uniform set of user interactions. It’s a nested hierarchy: interactions within platforms, platforms within user demographics, and so on.
In action
Imagine you work for a news company that supports a responsive web UI. During quarterly planning the UX researchers present survey results about how users are interested in location based news. Product analytics shows that almost all of the Mobile users have location tracking turned on but its not the case for desktop users. Can we check if the users who filled out the survey asking for location based news are mobile or desktop users?
We can use Multilevel Models to answer this question and decide if the feature will be easy ( implement for mobile users who already share location ) or hard ( implement for desktop users and bother them asking for more permissions ) because this type of data lends itself to a nested hierarchy: interactions within platforms, platforms within user demographics, and so on.
Bridging Individual and Aggregate Analysis
A key strength of multilevel modeling is its ability to connect individual-level behaviors with broader trends. In our case study, this means understanding how individual user interactions on a specific platform are influenced by overall trends in platform usage. Such insights are pivotal in tailoring user experiences that are not only efficient but also resonant with the user’s platform-specific expectations and habits.
Practical Application in Software Development
Employing multilevel models in software development translates to more informed decision-making. By understanding the nuances at each level of data (be it platform-specific, demographic, or individual preferences), developers can make targeted improvements. This leads to enhanced performance, increased user satisfaction, and ultimately, a more successful product.
5) Computational Algorithms in Statistics
Empowering Software Development with Statistical Algorithms
The Expectation-Maximization (EM) Algorithm
The EM algorithm is a great tool for handling incomplete datasets. Imagine you’re building a recommendation system, but your user data is incomplete. The EM algorithm allows you to estimate missing data points and refine your model iteratively, leading to more accurate and reliable recommendations.
The Metropolis-Hastings Algorithm
Originating from physics, the Metropolis-Hastings algorithm is useful for sampling from complex probability distributions — a task often encountered in machine learning and predictive modeling.
In software development, this translates to scenarios where you need to make predictions or decisions based on incomplete information. For instance, in predictive analytics, you might not know the exact distribution of your data. The Metropolis-Hastings algorithm allows you to generate samples from a distribution, even when you can’t explicitly define it, leading to more robust predictive models.
6) Robust Inference
Ensuring Reliable Outcomes in Software Development with Robust Inference
The Concept of Robust Inference
Robust inference is about making statistical conclusions that hold up even when the assumptions underlying the data analysis are not fully met.
Significance in Software Analytics
In software analytics, data often comes with irregularities: outliers, non-normal distributions, or heteroscedasticity (varying variability across data). Robust statistical methods are designed to withstand these irregularities, providing reliable insights that guide decision-making.
For example, when analyzing user engagement metrics, outliers (such as a few users with exceptionally high or low activity levels) can skew the results. Robust methods like using median instead of mean for central tendency or employing robust regression techniques can provide more accurate and insightful analyses.
Robust Methods in Predictive Modeling
In predictive modeling, common in software development for features like recommendation systems or user behavior prediction, robust inference ensures that the models perform well across a wide range of scenarios, not just under ideal conditions. This robustness enhances the model’s reliability and usability in real-world applications.
Key Practices for Robust Inference
- Data Exploration: Thoroughly explore and understand the data, including its limitations and potential issues.
- Choice of Methods: Use statistical methods known for their robustness to outliers and other data irregularities.
- Validation Techniques: Employ cross-validation and other techniques to ensure models are robust across different data sets and scenarios.
- Continuous Monitoring: Regularly monitor the performance of models and systems, ready to adjust as new data and insights emerge.
Incorporating robust inference into software development processes ensures that the decisions made, models built, and systems designed are reliable and effective, even under less-than-ideal circumstances. This approach leads to software solutions that are not only technically sound but also resilient and adaptable in the face of real-world challenges.
Conclusion:
In summary, the convergence of statistical understanding with software development practices offers a path to more sophisticated, reliable, and user-centric thinking. Embracing these methods will improve your odds of building great software.
