Companies are going to want to query their own internal documents – especially with the rise of LLMs and improvements in AI. Qxf2 has already heard of several CEOs that want to use AI/ML models to glean insights from internal knowledge stores. What does this mean for a tester? Well, you can expect to test such systems in the coming […]
Context-based question answering using LLM
Baseline Model Comparison for Performance Evaluation
Machine learning models evolve. As a tester, how do we know the newer version of the model is better? How do we know that the model did not get worse in other areas? The most intuitive approach would be to design a ‘good’ labelled dataset and then calculate the evaluation score like the F1 score for the model under test. […]
Testers, practice testing a machine learning algorithm!
Testers, you now have an app you can use to practice testing a machine learning algorithm. We have written and hosted a simple sentence classifier for testers to practice testing a machine learning algorithm. Play with it and let us know what fun bugs you discover. Hint: Testing this app is fun because there are several bugs that are not […]
Identify tech words from a text
As part of the pairing project activity at Qxf2 – pick a project that can produce a meaningful output in 5 hours and work on it collaborating with your team mates, I picked this project to identify the tech keywords from a text using NLTK module. This post covers the steps I followed to find the tech related words in […]
Scraping websites using Octoparse (no programming!)
Did you know you can scrape data from webpages without writing a single line of code? In this post, we will talk about a tool called Octoparse. We used Octoparse to scrape data from a list of URLs, without any coding at all. Data is valuable and it’s not always easy to get the correct data from the web sources […]
Organize and edit your test data with Quilt
We recently stumbled upon Quilt, a data package manager that wants to fill the role of ‘Github for data’. We have enjoyed using it so far. We think it can be useful for testers to manage their test data using Quilt. In this post, we will take an example dataset and show you step by step guide on how to […]
Quilt – a Data Package Manager
We have been testing data-rich applications for a long time. And like any experienced tester, we realize how difficult it is to create, maintain and update data every time the data model changes. So we were excited to come across Quilt, a data package manager, via Hacker News. We were thrilled that it integrated well with our favorite programming language […]
Getting started with Jupyter Notebooks
This post is for those who want to get started with Jupyter Notebooks. You don’t need to know anything beyond Python to start using Jupyter notebook. In this post I will be covering below topics. What is a Jupyter Notebook and app? How to install and use Jupyter Notebook Jupyter notebook user interface Sharing Jupyter Notebooks via GitHub Gist and […]
Extracting data from PDFs using Python
When testing highly data dependent products, I find it very useful to use data published by governments. When government organizations publish data online, barring a few notable exceptions, it usually releases it as a series of PDFs. The PDF file format was not designed to hold structured data, which makes extracting data from PDFs difficult. In this post, I will […]
Mockaroo Tutorial: Generate realistic test data
At a recent client engagement, my colleague had to create some location data for testing one of the client application. The data creation involved multiple steps like downloading some data from the net, removing some columns, transforming it as per needs using Python code. The process was a bit time-consuming. Recently when I was searching for some tools for data […]