3 ms·

I definitely think that GAs can uncover a variety of bugs ranging from simple NPEs and more nuanced bugs like memory leaks (which would then required human dev

by fizwhiz 12y ago

I definitely think that GAs can uncover a variety of bugs ranging from simple NPEs and more nuanced bugs like memory leaks (which would then required human dev intervention to investigate for a post-mortem) by dynamically generating the test-inputs. In addition to simply generating the input data, do you feel like GAs could broaden their span to essentially "mock" the states of other components in the system? I'm thinking of a case where you have some set of services deployed on different machines that communicate with each other. In theory, could we have the GA "mock/simulate" a network jitter sporadically (thus intercepting a request from ServiceA to ServiceB and deliberately dropping it)? This extends beyond the input data for some entry point at ServiceA, and instead encapsulates some sort of an "ether" surrounding all the components. Every permutation and combination of the subsytem state could in theory be controlled by the governing GA. If someone actually built a DSL/library that handled these things I'm sure it would benefit everybody in a remarkable way.

platz 12y ago
Also: "Genetic algorithms are not really an off-the-shelf black box that you can just plug your data into and get results." https://news.ycombinator.com/item?id=7712863 https://news.ycombinator.com/item?id=7712863
- fizwhiz 12y ago
  You post seems to be insinuating that we deem GAs as a panacea. No one here is saying that; to the contrary, we're just trying to see how we could use them for dynamically generating interesting test-input data. In addition to that, I'm just thinking out loud if you could possibly append to that functionality and see how to "prepare" more interesting test cases when multiple layers are involved. No one is disputing the fact that you need an expert to tune these to get the desirable result for hard problems. I argue that having a "good enough" understanding of GAs (i.e. you don't need a PhD in the subject) should be sufficient for you to solve simpler problems such as the one we're discussing. Do you have any counter-arguments to that? Can you cite any other examples where this view is challenged?

by fizwhiz 12y ago

platz 12y ago

Also: "Genetic algorithms are not really an off-the-shelf black box that you can just plug your data into and get results." https://news.ycombinator.com/item?id=7712863 https://news.ycombinator.com/item?id=7712863

fizwhiz 12y ago
You post seems to be insinuating that we deem GAs as a panacea. No one here is saying that; to the contrary, we're just trying to see how we could use them for dynamically generating interesting test-input data. In addition to that, I'm just thinking out loud if you could possibly append to that functionality and see how to "prepare" more interesting test cases when multiple layers are involved. No one is disputing the fact that you need an expert to tune these to get the desirable result for hard problems. I argue that having a "good enough" understanding of GAs (i.e. you don't need a PhD in the subject) should be sufficient for you to solve simpler problems such as the one we're discussing. Do you have any counter-arguments to that? Can you cite any other examples where this view is challenged?