If you are a tech company, and your people commit code, then you probably have some code review policy. And if you do not, you definitely should: you want to have an extra pair of eyes on the code that goes live. You certainly do not want a mistake to break things. And that is why you do pull requests to contribute to GitHub repos, and why Google employees must have a certain degree of maturity to commit code without review.
BUT, as long as that is a good idea, we must be careful to implement it the right way. Just enforcing reviews is not enough. You want to make the time between the code is sent for review and the code is deployed as short as possible. The longer the review time span is, the more work will be needed when the review comes. That is because:
- Who wrote the code simply does not have it fresh in their mind anymore. The context switch between the current task and the code he wrote days/weeks ago is just more demanding;
- Conflicts are more likely to arise, and then more work is needed in solving them;
- Other issues may depend on the code being held for review. Other people may spend (waste) time debugging an issue for which a solution is already available;
- If the repo is public, it makes more difficult for other to jump in and contribute, because they also have to be aware of all the pending code.
The right way to implement a code review system in a tech company
I think the best way to implement a code review system is to:
- Assign each code review to a particular member of the team. If everybody owns a task, then nobody does as well. That is why you want that particular review to be a responsibility of someone specific. An automated system can randomly assign a review to a team member.
- Each code review comes with a deadline. That’s it: code reviews are as important as any other task – basically because every other task often generates a code review at some point, so if we lag on those, nothing gets carried to the end and we are getting no work done at all! We may have different priorities associated with different deadlines, but we want each review to expire at some point (with the longest being a couple days)!
- Team members can turn down their assignments, but only if they have a good reason to. Again, if code does not get reviewed, it cannot go live, and thus the work has been done for nothing. Reviews are important and must be considered as such.
- Then just track how it goes: who is turning down most assignments? Is the weight uniformly distributed across the team?
Some months ago, I changed one link in the menu in my website postpaycounter.com. After that, it looked to me more people were purchasing products, i.e. the conversion rate had increased. But how to check whther that was really the case, or if it was just an accident/impression? Use an A/B test, I told myself!
With an A/B test, half of the users are served one version of the page, the one with the old link, and half of them another version of it, the one with the new link in place. When a sale happens, we may then log that as a success for the kind of page that was used, be it the A version or the B one.
In my case, the two versions of the page simply consisted of two different links in the menu, while I wanted the success to be logged when the user purchased something (I use Easy Digital Downloads to handle purchases).
I could find a bunch of plugins that allowed to set up A/B tests, but they all seemed pretty difficult to customize from a developer perspective, and I was already seeing myself wrestling with someone else’s code that provide tons of features useless to me, but through which was nearly impossible to interact with Easy Digital Downloads. So I decided to build my own, simple implementation, with the aim of it being tailored to developers rather than users who needed an interface.
An A/B test implementation example
This is an example of how to use the little framework. To set up a test, you only need to provide two functions:
During a Computational Vision lab, while comparing histograms, I stumbled upon a peculiar behavior. The histograms pairwise kernel matrix – which is just a fancy name for the matrix holding histograms correlations one with another – did not have ones on the diagonal. This means that one histogram was not fully correlated to itself, which is weird.
The comparison metric I was using is the simple histogram intersection one, defined as
The scope of this article is to present the one time pad cipher method and its biggest vulnerability: that of the many time pad.
The one time pad: what it is and how it works
The one time pad is the archetype of the idea of stream cipher. It’s very simple: if you want to make a message unintelligible to an eavesdropper, just change each character of the original message in a way that you can revert, but that looks random to another person.
The way the one time pad works is the following. Suppose is the clear-text message you would like to send securely, of length . First, you need to generate a string of equal length . Then, you can obtain a cipher-text version of your message by computing the bitwise XOR of the two strings:
The best thing is that decoding is just the same as encoding, as the XOR operator has the property that (and that ). The only difference is that the cipher-text is involved in the XOR, rather than the clear-text:
Below is an example of the one time pad encoding achieved with Python, with a made-up pad string.
In the first section, result holds the XOR result. In the second part, the result and one_time_pad variables are XORed together to obtain the original plain-text message again.
It is not difficult to realize that the whole strength of the algorithm lies in the pad. Of course, as an attacker, if you can obtain in some way, then it is not difficult to get the clear-text message from the ciphered one as well.
For a challenge in a university security class, I was given this file to crack: reverse1. I started with reverse0, which was considerably easier than the second one. In this post I will briefly explain how I tackled reverse1. I provided the files so you can you try on your own and then came back for hints if you are stuck! If you are new to this business, as I relatively am, I advise you to start from reverse0 and crack that first.
Hashes of reverse1 file:
MD5 – c22c985acb7ca0f373b7279138213158
SHA256 – cd56541a75657630a2a0c23724e55f70e7f4f77300faf18e8228cd2cffe8248e
Disassembling and hoping for the best
The first thing I did was to disassemble the file with Radare to have a look at the code.
#In a terminal
r2 -A ./reverse1
The assembly is quite jumbled up, and difficult to analyse all together. A quick look tells us that trying to crack the file just by reversing the assembly is no easy task, and actually a silly idea to begin with. There’s a cycle after the password is read from standard input, then some other instructions, then another cycle… it’s difficult to get what is going on…
Instead, let’s seek the Bad password print section, and see what should happen for the code to jump there. If we are lucky enough, we may find a bunch of final checks that will send over to the Bad password section. If we can find those, we may then look at those bits of assembly to understand how to avoid going there.
Scroll down enough, and down at the bottom I can see the Bad password part, starting at 0x080484f0.
Radare helps in showing two different arrows going into this address. The related comparisons are the following:
Well, I have been wondering about this for quite a while now, and I have tried to run some tests to better understand what’s going on under the hood. The standard answer is that after you call delete you should not expect anything good from accessing that memory spot. However, this did not seem enough to me. What is it really happening when calling delete(ptr)? Even though there no standard behavior, what could happen, anyway? Here’s what I’ve found. I’m using g++ on Ubuntu 16.04, so this may play a role in the results.
What I first expected when using the delete operator was that the freed memory would be handed back to the system for usage in other processes. Let me say this does not happen under any of the circumstances I have tried.
Memory released with delete still seem to be allocated to the program it first allocated it with new. I have tried, and there is no memory usage decrease after calling delete. I had a software which allocated around 30MB of lists through new calls, and then released them with subsequent delete calls. What happened is that, looking at the System monitor while the program was running, even a long sleep after the delete calls, memory consumption my the program was the same. No decrease! This means that delete does not release memory to the system.
In fact, it looks like memory allocated by a program is his forever! However, the point is that, if deallocated, memory can be used again by the same program without having to allocate any more. I tried to allocate 15MB, freeing them, and then allocating another 15MB of data after, and the program never used 30MB. System monitor always showed it around 15MB. What I did, in respect to the previous test, was just to change the order in which things happened: half allocation, half deallocation, other half of allocation.
So, apparently memory used by a program can increase, but never shrink. Continue reading