Difference between Retesting and Regression Testing

Retesting and regression testing are both statistical measures which are often used synonymously. However, there are many differences between the two terms.

Retesting is a method which is used to verify defect fix or fixes in a data which is being collected during a research. On the other hand, regression testing is used to check whether the defect fix or fixes have an impact on other propositions of the application which were working correctly before the changes in codes.

Furthermore, retesting involves the execution of test cases that had failed previously while regression testing engages the execution of passed test cases.


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    It is a statistical technique which is used to authenticate defect fix or fixes. It is planned to check the defect fixes which are listed in Build Notes. Besides, it executes previously failed test cases. In addition to that, it is engaged in re-running of the failed test cases which were linked with defect fixes (verified earlier).

    It simply helps to test specific part of an application without taking into account what impact it will create on the other parts.

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    Regression testing

    It is one of the most extensively used statistical method which helps to test the application subsequent to a change in its part. It is also used to understand the relationship amongst research variables. It illustrates how a dependent variable is related with an independent variable by showing them on a graph.

    Generally, regression testing is conducted to get knowledge about the change in the dependent variable resulting from one unit change in independent variable so as to forecast about the future outcomes.

    It can also be used to study casual relationships amongst dependent and independent variables. There are many different techniques which are used to conduct regression analysis, one of which is linear regression that is mainly used by firms to make predictions about their production and sales revenues. Some other methods may include, ordinary least squares regression and parametric one, both of  which have limited uses.

    Regression models are extensively used to forecast and predict future outcomes. Besides, it is always based on continuous independent variables. Additionally, there is only one error term in regression.

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