Q: Two engineers independently do quality assurance testing a large swath of code and discover \(e_1\) and \(e_2\) number of bugs of which \(e_c\) are common to both. The probability that each of them would find a bug given a large swath of code is \(p_1\) and \(p_2\) respectively. What is your best estimate of the number of unseen bugs in the code?A: This puzzle is inspired from W Feller's book on introduction to probability. The total number of unique bugs identified are \(e_1 + e_2 - e_c\). Let \(B_0\) represent the total number of bugs in the software application. We could make the following statements$$e_1 = p_1 \times B_0 \\e_2 = p_2 \times B_0 \\e_c = p_1 p_2 \times B_0 = \frac{e_1 e_2}{B_0}$$The unseen bugs are simply$$\text{Unseen Bugs} = B_0 - (e_1 + e_2 - e_c)$$Combining the above two equations yields$$\text{Unseen Bugs} = \frac{e_1 e_2}{e_c} - (e_1 + e_2 - e_c)$$which simplifies to$$\text{Unseen Bugs} = \frac{(e_1 - e_c)(e_2 - e_c)}{e_c}$$Notice, the final result is independent of \(B_0\). Obviously, this may not be accurate. Assume both engineers found exactly the same bugs, i.e. \(e_1 = e_2 = e_c\), then the number of unseen bugs would become 0 which need not always be true. Also, the above equation is undefined when \(e_c = 0\). Nevertheless, this does provide a good way to estimate the number of unseen bugs in software. The original example done by Polya & Feller were on proof readers reading text and spotting spell errors.You may also like A Bayesian Treasure Hunt If you are looking to buy some books in probability here are some of the best books to learn the art of ProbabilityThis book is a great compilation that covers quite a bit of puzzles. What I like about these puzzles are that they are all tractable and don't require too much advanced mathematics to solve.This is a book on algorithms, some of them are probabilistic. But the book is a must have for students, job candidates even full time engineers & data scientistsOverall an excellent book to learn probability, well recommended for undergrads and graduate studentsThis is a two volume book and the first volume is what will likely interest a beginner because it covers discrete probability. The book tends to treat probability as a theory on its ownA good book for graduate level classes: has some practice problems in them which is a good thing. But that doesn't make this book any less of buy for the beginner.A good book to own. Does not require prior knowledge of other areas, but the book is a bit low on worked out examples.An excellent resource (students, engineers and even entrepreneurs) if you are looking for some code that you can take and implement directly on the jobThis is a great book to own. The second half of the book may require some knowledge of calculus. It appears to be the right mix for someone who wants to learn but doesn't want to be scared with the "lemmas"This one is a must have if you want to learn machine learning. The book is beautifully written and ideal for the engineer/student who doesn't want to get too much into the details of a machine learned approach but wants a working knowledge of it. There are some great examples and test data in the text book too.This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read. Great for beginners. Some of the data on the companion website could be missing.Covered in this book are the central limit theorem and other graduate topics in probability. You will need to brush up on some mathematics before you dive in but most of that can be done onlineThis book has been yellow-flagged with some issues: including sequencing of content that could be an issue. But otherwise its good