Data corruption during testing

Data corruption during testing

How often do find corrupt data in testing databases. Sometimes corrupt data results in various system failures that were not really the code errors or bugs. This is a challenge that testers may face while testing an application that has data dependency.

Generally more than one tester works on these applications simultaneously and as we all know testers are good at modifying data as per our needs. In the process we modify data, perform our testing and then forget about the data that we modified it sounds so easy going and cute, correct? But if someone else picks up that modified data and performs another operation, there is a possibility that the testing may fail. Then we are happy that we found a bug, we log a bug, and approach a developer like a master telling him that there is a major bug in the system. The developer will then debug the code for the bug and find that there is nothing wrong with the code, he again approach you and say that the code is just fine. Then how did the bug arise?

The answer here is that the bugs have been encountered due to data discrepancy in the database. The code would just run fine on the correct data but as the data is incorrect, the code is unable to determine the correct behaviour that should be performed for such "discrepant" data

Data discrepancy is as serious issue as a major code error. Just imagine a newspaper saying "9000 people killed in bus accident" while it should say "9 people killed in a bus accident". The impact is huge. It’s just like presenting an incorrect application to the client. It may even result in a very angry client or a client laughing like it is taunting you. The end result is EMBARRASSMENT!!!

There are solutions to prevent data discrepancy in a database.

1. Take a backup: Before modifying any data in the database take a backup of the database to ensure that if any woolly mammoth is encountered we can make it disappear by a database restore.

2. Revert back data manually: If you think database backup and restore are too lengthy process and there is not enough time for such activity, just revert back the data that you modified to its original state. This can be done using an UPDATE SQL query.

3. Division of data: Data can be divided among the number of testers working on the application. This helps as each member performs functionality on the data provided to them and hence keeps the data very much clean.

4. Minimal Use of Update and Insert: While using SQL, the use of update and insert should be minimal and should be used only if required. Don't just use update and insert just for fun in the testing database.

5. Ask before you do: One should always ask a DBA or the senior database analyser for any changes being made to the testing database. This will ensure that the DBA is aware of the changes and can handle any data related discrepancy in the database.

Thanks,

Mohit Bhatia

Mutation Testing

Mutation testing

Mutation Testing involves running slightly corrupted versions of your target program through your test suite to see if any test cases flag the variations as defects. It evaluates the quality of software tests. Mutation testing involves modifying a program's source code or byte code in small ways. A test suite that does not detect and reject the mutated code is considered defective. These so-called mutations are based on well-defined mutation operators that either mimic typical programming errors (such as using the wrong operator or variable name) or force the creation of valuable tests (such as driving each expression to zero). The purpose is to help the tester develop effective tests or locate weaknesses in the test data used for the program or in sections of the code that are seldom or never accessed during execution.

It also answers following questions:-

·         How do you know that you can trust your unit tests? 

·         How do you know that they’re really telling you the truth? 

·         If they don't find a bug, does that really mean that there aren't any? 

·         What if you could test your tests?

Mutation testing is one way that you can test those tests.  Mutation testing involves deliberately altering a program’s code, then re-running a suite of valid unit tests against the mutated program.  A good unit test will detect the change in the program and fail accordingly.

Aim

Tests can be created to verify the correctness of the implementation of a given software system, but the creation of tests still poses the question whether the tests are correct and sufficiently cover the requirements that have originated the implementation. Mutation testing was pioneered in the 1970s to locate and expose weaknesses in test suites. The theory was that if a mutation was introduced without the behavior (generally output) of the program being affected, this indicated either that the code that had been mutated was never executed (redundant code) or that the testing suite was unable to locate the injected fault. In order for this to function at any scale, a large number of mutations had to be introduced into a large program, leading to the compilation and execution of an extremely large number of copies of the program. This problem of the expense of mutation testing had reduced its practical use as a method of software testing, but the increased use of object oriented programming languages and unit testing frameworks has led to the creation of mutation testing tools for many programming languages as a means to test individual portions of an application.

Mutation testing overview

Mutation testing is done by selecting a set of mutation operators and then applying them to the source program one at a time for each applicable piece of the source code. The result of applying one mutation operator to the program is called a mutant. If the test suite is able to detect the change (i.e. one of the tests fails), then the mutant is said to be killed.

For example, consider the following C++ code fragment:

if (a && b) {

    c = 1;

} else {

    c = 0;

}

The condition mutation operator would replace && with || and produce the following mutant:

if (a || b) {

    c = 1;

} else {

    c = 0;

}

Now, for the test to kill this mutant, the following condition should be met:

§  Test input data should cause different program states for the mutant and the original program. For example, a test with a = 1 and b = 0 would do this.

§  The value of 'c' should be propagated to the program's output and checked by the test.

Weak mutation testing (or weak mutation coverage) requires that only the first condition is satisfied. Strong mutation testing requires that both conditions are satisfied. Strong mutation is more powerful, since it ensures that the test suite can really catch the problems. Weak mutation is closely related to code coverage methods. It requires much less computing power to ensure that the test suite satisfies weak mutation testing than strong mutation testing.

Equivalent mutants

Many mutation operators can produce equivalent mutants. For example, consider the following code fragment:

int index = 0;

while (…)

{

    …;

    index++;

    if (index == 10) {

        break;

    }

}

Boolean relation mutation operator will replace == with >= and produce the following mutant:

int index = 0;

while (…)

{

    …;

    index++;

    if (index >= 10) {

        break;

    }

}

However, it is not possible to find a test case that could kill this mutant. The resulting program is equivalent to the original one. Such mutants are called equivalent mutants.

Equivalent mutants detection is one of biggest obstacles for practical usage of mutation testing. The effort needed to check if mutants are equivalent or not, can be very high even for small programs.

Mutation operators

A variety of mutation operators were explored by researchers. Here are some examples of mutation operators for imperative languages:

§  Statement deletion.

§  Replace each boolean subexpression with true and false.

§  Replace each arithmetic operation with another one, e.g. + with *, - and /.

§  Replace each boolean relation with another one, e.g. > with >=, == and <=.

§  Replace each variable with another variable declared in the same scope (variable types should be the same).

These mutation operators are also called traditional mutation operators. Beside this, there are mutation operators for object-oriented languages, for concurrent constructions, complex objects like containers etc. They are called class-level mutation operators. For example the MuJava tool offers various class-level mutation operators such as: Access Modifier Change, Type Cast Operator Insertion, and Type Cast Operator Deletion. Moreover, mutation operators have been developed to perform security vulnerability testing of programs.

Thanks,

Mohit Bhatia