Fuzzing - Wikipedia. Fuzzing or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer program. The program is then monitored for exceptions such as crashes, or failing built- in code assertions or for finding potential memory leaks. Typically, fuzzers are used to test programs that take structured inputs. ![]() Cumulative Flow Diagram present information about status of work but also it helps to spot issues that a team may be facing. This is real power of CFD. Writing effective test cases is a skill and that can be achieved by some experience and in-depth study of the application on which test cases are being written. Here. SoftwareCPR provides these FDA related software and computer news items. Click What's New at CDRH, What's New at CDER, or What's New at CBERto see new items listed on. ![]() This structure is specified, e. An effective fuzzer generates semi- valid inputs that are "valid enough" in that they are not directly rejected by the parser, but do create unexpected behaviors deeper in the program and are "invalid enough" to expose corner cases that have not been properly dealt with. For the purpose of security, input that crosses a trust boundary is often the most interesting.[1] For example, it is more important to fuzz code that handles the upload of a file by any user than it is to fuzz the code that parses a configuration file that is accessible only to a privileged user. History[edit]The fuzzing of programs with random inputs dates back to the 1. Programmers would use punched cards that were pulled from the trash or card decks of random numbers as input to computer programs. If an execution revealed undesired behavior, a bug had been detected and was fixed. The execution of random inputs is also called random testing or monkey testing. In 1. 98. 1, Duran and Ntafos formally investigated the effectiveness of testing a program with random inputs.[3][4] While random testing had been widely perceived to be the worst means of testing a program, the authors could show that it is a cost- effective alternative to more systematic testing techniques. In 1. 98. 3, Steve Capps developed "The Monkey", a tool that would generate random inputs for classic Mac OS applications, such as Mac. Paint.[5] The figurative "monkey" refers to the infinite monkey theorem which states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will eventually type out the entire works of Shakespeare. In the case of testing, the monkey would write the particular sequence of inputs that will trigger a crash. The term "fuzzing" originates from a 1. Barton Miller at the University of Wisconsin.[6] To fuzz test a Unix utility meant to automatically generate random files and command- line parameters for the utility. The project was designed to test the reliability of Unix programs by executing a large number of random inputs in quick succession until they crashed. It also provided early debugging tools to determine the cause and category of each detected failure. To allow other researchers to conduct similar experiments with other software, the source code of the tools, the test procedures, and the raw result data were made publicly available.[7] Later, the term fuzzing was not limited only to command- line utilities. In 1. 99. 1, the crashme tool was released, which was intended to test the robustness of Unix and Unix- likeoperating systems by executing random machine instructions.[8]In 1. GUI- based tools (such as the X Window System), network protocols, and system library APIs.[9]In April 2. Google announced Cluster. Fuzz, a cloud- based fuzzing infrastructure for security- critical components of the Chromium web browser.[1. Security researchers can upload their own fuzzers and collect bug bounties if Cluster. Fuzz finds a crash with the uploaded fuzzer. In September 2. 01. Shellshock[1. 1] was disclosed as a family of security bugs in the widely used Unix. Bashshell; most vulnerabilities of Shellshock were found using the fuzzer AFL.[1. Many Internet- facing services, such as some web server deployments, use Bash to process certain requests, allowing an attacker to cause vulnerable versions of Bash to execute arbitrary commands. This can allow an attacker to gain unauthorized access to a computer system.[1. In April 2. 01. 5, Hanno Böck showed how the fuzzer AFL could have found the 2. Heartbleed vulnerability.[1. The Heartbleed vulnerability was disclosed in April of 2. It is a serious vulnerability that allows adversaries to decipher otherwise encrypted communication, for instance, during online banking. The vulnerability was accidentally introduced into Open. SSL which implements the https- protocol for secure communication and is used by the majority of servers on the internet. Shodan reported 2. April 2. 01. 6[1. January 2. 01. 7.[1. In August 2. 01. 6, the Defense Advanced Research Projects Agency (DARPA) held the finals of the first Cyber Grand Challenge, a fully automated capture- the- flag competition that lasted 1. The objective was to develop automatic defense systems that can discover, exploit, and correct software flaws in real- time. Fuzzing was used as an effective offense strategy to discover flaws in the software of the opponents. It showed tremendous potential in the automation of vulnerability detection. The winner was a system called "Mayhem"[1. For. All. Secure led by David Brumley. In September 2. 01. Microsoft announced Project Springfield, a cloud- based fuzz testing service for finding security critical bugs in software.[2. In December 2. 01. Google announced OSS- Fuzz which allows for continuous fuzzing of several security- critical open- source projects.[2. Types of fuzzers[edit]A fuzzer can be categorized as follows: [9][1]A fuzzer can be generation- based or mutation- based depending on whether inputs are generated from scratch or by modifying existing inputs,A fuzzer can be dumb or smart depending on whether it is aware of input structure, and. A fuzzer can be white- , grey- , or black- box, depending on whether it is aware of program structure. Reuse of existing input seeds[edit]A mutation- based fuzzer leverages an existing corpus of seed inputs during fuzzing. It generates inputs by modifying (or rather mutating) the provided seeds. For example, when fuzzing the image library libpng, the user would provide a set of valid PNG image files as seeds while a mutation- based fuzzer would modify these seeds to produce semi- valid variants of each seed. The corpus of seed files may contain thousands of potentially similar inputs. Automated seed selection (or test suite reduction) allows to pick the best seeds in order to maximize the total number of bugs found during a fuzz campaign.[2. A generation- based fuzzer generates inputs from scratch. For instance, a smart generation- based fuzzer[2. Unlike mutation- based fuzzers, a generation- based fuzzer does not depend on the existence or quality of a corpus of seed inputs. Some fuzzers have the capability to do both, to generate inputs from scratch and to generate inputs by mutation of existing seeds.[2. Aware of input structure[edit]Typically, fuzzers are used to generate inputs for programs that take structured inputs, such as a file, a sequence of keyboard or mouse events, or a sequence of messages. This structure distinguishes valid input that is accepted and processed by the program from invalid input that is quickly rejected by the program. What constitutes a valid input may be explicitly specified in an input model. Examples of input models are formal grammars, file formats, GUI- models, and network protocols. Even items not normally considered as input can be fuzzed, such as the contents of databases, shared memory, environment variables or the precise interleaving of threads. An effective fuzzer generates semi- valid inputs that are "valid enough" so that they are not directly rejected from the parser and "invalid enough" so that they might stress corner cases and exercise interesting program behaviours. A smart (model- based,[2. For instance, if the input can be modelled as an abstract syntax tree, then a smart mutation- based fuzzer[2. If the input can be modelled by a formal grammar, a smart generation- based fuzzer[2. However, generally the input model must be explicitly provided which is difficult when it is proprietary, unknown, or very complex. If a large corpus of valid and invalid inputs are available, a grammar induction technique, such as Angluin's L* algorithm would be able to generate an input model.[2. A dumb fuzzer[6][2. For instance, AFL is a dumb mutation- based fuzzer that modifies a seed file by flipping random bits, by substituting random bytes with "interesting" values, and by moving or deleting blocks of data. However, a dumb fuzzer might generate a lower proportion of valid inputs and stress the parser code rather than the main components of a program. Unit Testing and Coding Best Practices for Unit Tests: A Test- Driven Perspective. Unit testing is an essential instrument in the toolbox of any serious software developer. However, it can sometimes be quite difficult to write a good unit test for a particular piece of code. Having difficulty testing their own or someone else’s code, developers often think that their struggles are caused by a lack of some fundamental testing knowledge or secret unit testing techniques. In this article, I would like to show that unit tests are quite easy; the real problems that complicate unit testing, and introduce expensive complexity, are a result of poorly- designed, untestable code. We will discuss what makes code hard to test, which anti- patterns and bad practices we should avoid to improve testability, and what other benefits we can achieve by writing testable code. We will see that writing testable code is not just about making testing less troublesome, but about making the code itself more robust, and easier to maintain. What is Unit Testing? Essentially, a unit test is a method that instantiates a small portion of our application and verifies its behavior independently from other parts. A typical unit test contains 3 phases: First, it initializes a small piece of an application it wants to test (also known as the system under test, or SUT), then it applies some stimulus to the system under test (usually by calling a method on it), and finally, it observes the resulting behavior. If the observed behavior is consistent with the expectations, the unit test passes, otherwise, it fails, indicating that there is a problem somewhere in the system under test. These three unit test phases are also known as Arrange, Act and Assert, or simply AAA. A unit test can verify different behavioral aspects of the system under test, but most likely it will fall into one of the following two categories: state- based or interaction- based. Verifying that the system under test produces correct results, or that its resulting state is correct, is called state- based unit testing, while verifying that it properly invokes certain methods is called interaction- based unit testing. As a metaphor for a proper unit test, imagine a mad scientist who wants to build some supernatural chimera, with frog legs, octopus tentacles, bird wings, and a dog’s head. This metaphor is pretty close to what programmers actually do at work). How would that scientist make sure that every part (or unit) he picked actually works? Well, he can take, let’s say, a single frog’s leg, apply an electrical stimulus to it, and check for proper muscle contraction. What he is doing is essentially the same Arrange- Act- Assert steps of the unit test; the only difference is that, in this case, unit refers to a physical object, not to an abstract object we build our programs from. I will use C# for all examples in this article, but the concepts described apply to all object- oriented programming languages. A simple unit test could look like this: [Test. Method]. public void Is. Palindrome_For. Palindrome. String_Returns. True(). In the Arrange phase, we create and set up a system under test. A system under test could be a method, a single object, or a graph of connected objects. It is OK to have an empty Arrange phase, for example if we are testing a static method - . SUT already exists in a static form and we don't have to initialize anything explicitly. Palindrome. Detector detector = new Palindrome. Detector(). // The Act phase is where we poke the system under test, usually by invoking a method. If this method returns something back to us, we want to collect the result to ensure it was correct. Or, if method doesn't return anything, we want to check whether it produced the expected side effects. Palindrome = detector. Is. Palindrome("kayak"). The Assert phase makes our unit test pass or fail. Here we check that the method's behavior is consistent with expectations. Assert. Is. True(is. Palindrome). Unit Tests vs. Integration Tests. Another important thing to consider is the difference between unit and integration tests. The purpose of a unit test is to verify the behavior of a relatively small piece of software, independently from other parts. Unit tests are narrow in scope, and allow us to cover all cases, ensuring that every single part works correctly. On the other hand, integration tests demonstrate that different parts of a system work together in the real- life environment. They validate complex scenarios (we can think of integration tests as a user performing some high- level operation within our system), and usually require external resources, like databases or web servers, to be present. Let’s go back to our mad scientist metaphor, and suppose that he has successfully combined all the parts of the chimera. He wants to perform an integration test of the resulting creature, making sure that it can, let’s say, walk on different types of terrain. First of all, the scientist must emulate an environment for the creature to walk on. Then, he throws the creature into that environment and pokes it with a stick, observing if it walks and moves as designed. After finishing a test, the mad scientist cleans up all the dirt, sand and rocks that are now scattered in his lovely laboratory. Notice the significant difference between unit and integration tests: A unit test verifies the behavior of small part of the application, isolated from the environment and other parts, and is quite easy to implement, while an integration test covers interactions between different components, in the close- to- real- life environment, and requires more effort, including additional setup and teardown phases. A reasonable combination of unit and integration tests ensures that every single unit works correctly, independently from others, and that all these units play nicely when integrated, giving us a high level of confidence that the whole system works as expected. However, we must remember to always identify what kind of test we are implementing: a unit or an integration test. The difference can sometimes be deceiving. If we think we are writing a unit test to verify some subtle edge case in a business logic class, and realize that it requires external resources like web services or databases to be present, something is not right — essentially, we are using a sledgehammer to crack a nut. And that means bad design. What Makes a Good Unit Test? Before diving into the main part of this tutorial, let’s quickly discuss the properties of a good unit test. A good unit test should be: Easy to write. Developers typically write lots of unit tests to cover different cases and aspects of the application’s behavior, so it should be easy to code all of those test routines without enormous effort. Readable. The intent of a unit test should be clear. A good unit test tells a story about some behavioral aspect of our application, so it should be easy to understand which scenario is being tested and — if the test fails — easy to detect how to address the problem. With a good unit test, we can fix a bug without actually debugging the code! Reliable. Unit tests should fail only if there’s a bug in the system under test. That seems pretty obvious, but programmers often run into an issue when their tests fail even when no bugs were introduced. For example, tests may pass when running one- by- one, but fail when running the whole test suite, or pass on our development machine and fail on the continuous integration server. These situations are indicative of a design flaw. Good unit tests should be reproducible and independent from external factors such as the environment or running order. Fast. Developers write unit tests so they can repeatedly run them and check that no bugs have been introduced. If unit tests are slow, developers are more likely to skip running them on their own machines. One slow test won’t make a significant difference; add one thousand more and we’re surely stuck waiting for a while. Slow unit tests may also indicate that either the system under test, or the test itself, interacts with external systems, making it environment- dependent. Truly unit, not integration. As we already discussed, unit and integration tests have different purposes. Both the unit test and the system under test should not access the network resources, databases, file system, etc., to eliminate the influence of external factors. That’s it — there are no secrets to writing unit tests.
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