In today’s world, software engineering is a rapidly growing field, with more and more people joining it every day. However, traditional hiring practices in the tech industry have been shown to be biased and not inclusive of all potential candidates, particularly women and those without a degree in computer science. FAANG companies, or the big tech giants (Facebook, Apple, Amazon, Netflix, and Google), have been particularly criticised for their outdated and non-inclusive recruitment practices. In this article, we will discuss how to test effectively software engineers in a more inclusive way than FAANG companies generally do.
Why are the current testing practices in the tech industry biased and not inclusive?
Many software engineering tests, particularly those used by FAANG companies, focus heavily on
algorithmic problem-solving. While these tests may be useful in evaluating a candidate’s technical abilities, they do not necessarily reflect the skills needed to succeed in a real-world engineering role. This is particularly true for women and those without a computer science degree, who may not have been exposed to the same level of algorithmic problem-solving as their male peers.
In a previous blog post, we tell the true story of how men became the dominant archetype in the world of coding from the late 1960’s, after it had been a vocation almost exclusively populated by women. This, along with other more complex gender norms and social constructs, has influenced the number of women choosing to study STEM subjects and in particular, Computer Science. Women make up around 16% of Computer Science graduates in Australia and it’s similar in the UK and US. Therefore, where tests are heavily focused on computer science fundamentals and algorithmic problem-solving, men are more likely than women to pass them and will find it easier to enter those test environments – ie without as much pre-test prep’.
How can organisations create a more inclusive testing process?
companies should focus on evaluating a candidate’s skills based on real-world scenarios. This can include evaluating their ability to work on projects collaboratively, their communication skills, and their ability to solve real-world engineering problems. For example, instead of asking a candidate to solve a complex algorithmic problem, they could be given a real-world engineering challenge that requires collaboration with others on the team. This approach would allow the candidate to demonstrate their skills in a more inclusive and relevant way.
Another way to create a more inclusive testing process is to use alternative evaluation methods that are less biased. For example, using code reviews or pair programming sessions to evaluate a candidate’s technical skills can be less biased than traditional algorithmic problem-solving tests. These methods allow candidates to demonstrate their skills in a real-world setting and also help to identify any knowledge gaps that may need to be addressed.
Take home test vs in-interview tests – a widely debated question. In order to be inclusive, whichever you
opt for must be equitable. Consider the memes that depict the difference between equal and equitable. There’s a tree with apples in it and two people, both with equal opportunity, to pick the apples but one has a long ladder and so can reach all the apples, and one has a small ladder and therefore can only reach the lower hanging fruit. In the same way, women and men can have the same opportunity to complete the test but one has advantages over the other.
Remember that:
Around 76% more men undertake a computer science degree than women and women shoulder around 21 hrs per week more unpaid caring and household work than men. You don’t need to be a genius to see where the inequity lies in compsci-heavy tests and those that can take literally days to complete (in one’s “spare” time).
All of this said it’s also essential to make the whole recruitment process more transparent and inclusive. This means ensuring that the interview process is consistent and fair for all candidates. Companies should also make sure that they are recruiting from a diverse range of sources, including universities and coding bootcamps that have a high percentage of female and underrepresented minority students. Finally, when hiring more experienced candidates, remove requirements for a computer science degree or background and instead focus on the types of engineering problems that have been worked on.
At Project F, we work with many progressive organisations that want to take a systemic approach to achieving gender balance in their technology teams and leadership. One of the key focus areas in all tech DEI work is a recruitment process that is as bias-free as possible.
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