Skip to main content

Testing In Python...and my Two cents

The only lesson I have learned in mys past one and half of years of experience in testing is that the easier I make writing and running tests for my programs, the faster I can produce bug-free software.

Being a developer and also being a tester can do wonders I think. Never before this thought would have crossed my mind while coding.


That is why I start most projects nowadays with two pre-baked files: main.py and tests.py.
With this setup and tools like Nose, testing code in Python for me is nearly pain free. Recently however, I notice one aspect of the unittest package in Python's standard libary drives me absolutely crazy:
Why is there no simple method to run a single test case from within an interactive python shell?
As developers, this is important because if we are going to make writing tests an integral part of our development workflow, one needs some way to actually run the damn test without dropping out of our Python sessions and breaking out of our mental flow.
Being forced to run tests solely from the commandline also means we can't take advantage of features like ipython's ability to automatically drop into a pdb debugging session when an error occurs. This is extremely useful for when you want to introspect one or two variables to determine why a test failure occurred.
In general, it seems like the unittest library's lack of a simple function to run single test cases in an interactive shell dis-incentivizes programmers from writing tests and detracts from python's whole "rapid iteration" ethos.
After googling around and failing to find any good alternatives, I've finally settled on writing my own utility function to run single test cases:

def runtest(testCase, methodName):
    """ Runs a test case from within interactive shell """

    tc = testCase(methodName)
    getattr(tc, "setUp", lambda:None)()
    try:
        getattr(tc, methodName)()  
    finally:
        getattr(tc, "tearDown", lambda:None)()

I add this function to every single project that I create now, and the qualitative feel of writing tests just feels so much more natural now. I also find myself writing more tests and running them more often with this function in my projects.
Give it a try, and let me know whether this has any effect for you. I'd love to hear your feedback on how it affects your development workflow.

Comments

Post a Comment

Popular posts from this blog

Racecraft (Project Koru) · Prologue — The Origin Story

Racecraft · Prologue , The Origin Story It Started With a Wine List and a Question About Racing How a happy-hour conversation in the Bay Area turned into a trustable AI race coach , and then into a second version that runs entirely on a phone, on the NPU. This is the prologue to a five-part series. Two years ago(1st November, 2024) I was in the Bay Area for a GDE Summit. If you've never been: it's a couple of days of talks among Google Developer Experts, the kind of people who get unreasonably excited about a new on-device runtime, and then , mercifully , a happy hour where everyone stops performing and just eats. We ended up at a restaurant(Puesto Santa Clara), a long table of GDEs, and I was doing the most important engineering of the evening: trying to decide which wine to order. Across the table was Ajeet Mirwani . I don't even remember how the wine talk turned into racing talk , these things drift , but the moment the word "racing" ...
Post a Comment
Read more

The Throughput Trap: Benchmarking vLLM on OpenXLA and the Reality of Production LLM Serving

vLLM Systems · DevLab 2026, Deep Dive I was recently invited by the Google TPU team to speak at the OpenXLA Summer DevLab 2026 . This post breaks down our deep-dive evaluation of the matured vLLM + OpenXLA stack, the fundamental engineering mismatches between CUDA and XLA serving paths, and why traditional capacity metrics are lying to you. If you are operating large language models at enterprise scale right now, your platform architecture team is likely staring at a massive infrastructure crossroads: Should we migrate our core serving workloads from GPUs to TPUs? Historically, NVIDIA's CUDA ecosystem was the only serious option for user-facing, low-latency LLM generation. But here in 2026, the economics and infrastructure options have transformed. Google TPUs are highly available, cheaper per chip, and the open-source serving stack built around vLLM and OpenXLA has officially achieved absolute production readiness. Yet, when our infrast...
Post a Comment
Read more

A Split‑Brain Neuro‑Symbolic Training Method for High‑Velocity Autonomous Coaching from Telemetry

 Author: Rabimba Karanjai Scope: Problem statement + data methodology + model training (no deployment discussion) Abstract Real‑time coaching in motorsport is a safety‑critical learning problem : a system must map noisy, high‑frequency telemetry to short, actionable guidance that remains physically consistent and avoids hazardous recommendations . This paper proposes a “Split‑Brain” training formulation that separates (i) a semantic coaching target (what action/critique should be expressed) from (ii) a reflexive interface (how actions are represented as compact, verifiable tokens). The approach trains a Small Language Model (SLM) in the Gemma family [1] using QLoRA fine‑tuning [2] , and introduces a telemetry tokenizer plus teacher‑student synthesis pipeline to generate instruction‑action pairs at scale. Core contribution: a reproducible method to convert “ golden lap ” differential tel...
Post a Comment
Read more