Now There are some specific rules to input the

Now a days software technology is increasing day byday and the risks prone to it are also increasing exponentially. So, there is needto think about the security for the software. According to one survey reportthere is loss of several thousands of dollars for one security breach. In orderto overcome these security risks there should be some testing software to testthe vulnerabilities and report them, one of them is fuzz testing.Fuzz testing is a technique which inputs random datato a particular software, in order to know the vulnerabilities of thatsoftware. There are some specific rules to input the code so that it works in aexpected manner, that is code should be some what similar so that it passesconsistency checks, it also should be variant so that it causes exceptionalbehaviour and leads to crash of the interpreter. LangFuzz is the technique thatfollows the above specified rules.

LangFuzz has a speciality that it isflexible and takes the grammar as input, generates programs according to thegiven input, it also requires 3 inputs, a language grammar, a sample code and atest suite. There are also language specific fuzzers like CSmith, whichoperates on C-programming language grammar. When we compare these both werecognise that unlike LangFuzz, CSmith aims on correctness of the bugs,alternative for security bugs.             Thereis another fuzzing tool called jsfunfuzz, which is used on javascript, is veryeffective tool and discovered many bugs in java script engine web browsers. Itis a black box testing tool, like LangFuzz, so it also identifies correctnesserrors. There are many fuzzers which targets specific functionalities in webbrowsers and find the bugs. e.g.

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, crossfuzz, ref fuzz,mangleme, canvas fuzzer and transfuzz.                        If observed, the working of LangFuzz, it usesthe two methods of approaches called Generative approach and Mutative approach.Though it is language independent, it should follow some language semanticrules in order to be implemented effectively. In code generation, languagegrammar is used to generate code fragments, these code fragments should beadjusted to the environment in order that there is no error even if identifiedby the identifiers. There are two phases in the code mutation, a learning phasewhere set of files are processed with help of parser, then non terminals aregenerated, so the aim to identify bugs becomes easier, in main mutation phase aparticular file is processed several times by a parser. After this LangFuzzstarts the implementation process, here to parse the input code ANTLR frameworkis used. Next is code generation, in this a step wise elaboration algorithm isused to produce code fragments and these fragments are to adjusted to present environment.

Later mapping is done with names of identifiers. If identifiers maps one objectto another object, all the properties of first object are transferred to laterone.            To run a mutation test, LangFuzz should run the test onits proper test harness. For example Mozilla test suite has a file shell.

jswith all properties and to run it, javascript engine  should execute all shell files in particularorder. LangFuzz implements this test suite effectively  for different test frameworks. Here there isa problem, process of starting the javascript engines is very slow and if werepeat the same process several times, it consumes large amount of computationtime. For this LangFuzz uses a javascript program called driver, when startedwith test harness, it reduces the number of scripting engines and gives a signwhen finished. LangFuzz also have several parameters that implements decisionsduring the running process, these parameters have default values and theevaluation of definite parameter set consumes huge amounts of time nearly 3-4days per set and this set has to be repeated several hundred times.

To know theeffectiveness of LangFuzz, three types of evaluation process is done. 1.External validation, 2.

Internal validation and 3. Field study. Here we compareLangFuzz and jsfunfuzz, when the both tools are run on the same environment,i.e.

, same windows, same time and the CPU and RAM are distributed equally. Somedefects found are common in both, so we can say they overlap and effectivenessis calculated as the ratio of defects found by LangFuzz to the defects found byjsfunfuzz. This is compared by Mozilla’s TraceMonkey, because it is publiclyavailable and data is provided by Mozilla team, can be used directly. Intesting for example, a bug is introduced in a testing window after few minutesbug is detected by jsfunfuzz and that bug is fixed at a point of stage, processis repeated several times. In external validation jsfunfuzz detected 23 defectsof which 15 were present in the testing window. Langfuzz detected 26 defects ofwhich 8 were present in the same testing window. It is clear that LangFuzz hasdetected more number of defects outside the testing window. By the output resultit is clear that Langfuzz is 53% as effective as jsfunfuzz.

In internalvalidation 2 testing windows are taken to compare the results, it is observedthat for generation configuration in one window identified the defects 6 timesmore than the other window, whereas the defects identified by mutationconfiguration is almost same for the both windows. But both methods togetherdetect more defects, so if done together LangFuzz will be very effective.  Infield tests, for real time defects, LangFuzz is applied to 3 differentinterpreter engines namely MozillaTraceMonkey, Google V8 and the PHP engine. LangFuzz has detected 39% securityissues for Mozilla TraceMonkey, 19% for Google V8 and defects for PHP were notsecurity related. Within time period of 120 days, Langfuzz identified 164 realworld bugs and 31 security related defects in javascript engines, while on PHPit identified 20 defects in 14 days. Some adaptations for LangFuzz arenecessary so that it also be effective for PHP for this generation of parserusing ANTLR has to be done.             There is no generalization thatLangFuzz identifies the defects in every interpreter, every language and sometimesit may not meet the particular properties and might not be effective.

There isalso case that all the defects identified by it are not real and there might besome duplicates. Toconclude, Fuzz testing is the process which is very easy to implement, ifprovided with particular language and particular semantic rules it is veryeffective. In this Langfuzz is method which is adaptable easily. It identifiesthe potential software vulnerabilities very quickly. As it works very well forreal time implementations several web browsers use this technique.

It uses thesimple and small algorithms like ‘Shortest terminal string’ and once the run testis triggered then there is no need of manual interaction, it automaticallyidentifies the bug and reports the defects.