Utilizing semantic clusters to Predict Software defects

Abstract

A method, apparatus and product for utilizing semantic clusters to predict software defects. The method comprising: obtaining a plurality of software elements that are associated with a version of a System Under Test (SUT), wherein the plurality of software elements comprise defective software elements which are associated with a defect in the version of the SUT; defining, by a processor, a plurality of clusters, wherein each cluster of the plurality of clusters comprises software elements having an attribute, wherein the attribute is associated with a functionality of the SUT; and determining a score of each cluster of the plurality of clusters, wherein the score of a cluster is based on a relation between a number of defect software elements in the cluster and a number of software elements in the cluster.

Description

TECHNICAL FIELD[0001]The present disclosure relates to software engineering in general, and to software testing, in particular.BACKGROUND[0002]Software defects caused by coding errors occur through the processes of a programmer making a mistake, which results in a defect in the software source code. If this defect is executed, in certain situations the system will produce wrong results, causing a failure. Not all defects will necessarily result in failures (e.g. defects in dead code). However, a defect can turn into a failure when the environment is changed. Examples of these changes in environment include the software being run on a new computer hardware platform, alterations in source data, or interacting with different software. A single defect may result in a wide range of failure symptoms.[0003]Clearly the increased complexity of software systems nowadays amplifies the probability of software defects, for various reasons. However, there is also a constant pressure to reduce sof...

AI Technical Summary

Problems solved by technology

Software defects caused by coding errors occur through the processes of a programmer making a mistake, which results in a defect in the software source code.

If this defect is executed, in certain situations the system will produce wrong results, causing a failure.

Not all defects will necessarily result in failures (e.g. defects in dead code).

However, a defect can turn into a failure when the environment is changed.

A single defect may result in a wide range of failure symptoms.

Clearly the increased complexity of software systems nowadays amplifies the probability of software defects, for various reasons.

However, there is also a constant pressure to reduce software development time and increase the quality of the software.

However, such methodology does not help in identifying error prone elements whose defects were not yet discovered.

That methodology is also not helpful in identifying the more error prone elements out of a collection of new elements introduced to the software.

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