features froma con guration management archive continuously applies the

features but it doesn’t integrate with graphic front-end DDD. This isa typical example of Yesterday my program worked, today it does not” prob-lem. In order to nd the cause this problem, di erences between old version’sand newer version’s con gurations could be a great starting state which is alsocalled delta debugging. But one programmer can produce so many changes thatthese di erences are practically too large for any person. But, delta debuggingbecomes an alternative when the di erences can be narrowed down automati-cally. Ness and Ngo’s method for compiler development, ordered changes froma con guration management archive continuously applies the changes until itsregression testing fails.Regression containment’s linear search is not sucient in below scenarios:{ Interference : A combination of several changes.{ Inconsistency : con gurations combinations of changes that do not result ina testable program.{ Granularity : one needs facilities to break changes into smaller chunks.2 Automated Delta Debuggingdd+ algorith, an automated delta debugging techniques is presented which gen-eralize regression containment such that interference, inconsistencies, and gran-ularity problems are dealt. this method includes detects arbitrary interferences,detects individual failure-inducing changes and handles inconsistencies. Our goalis to nd minimal failure-inducing change set.If con guration does not ful ll anyof its speci c properties, then there are probabilities that one of its subsets satisfythem. Thus, below divide and conquer algorithm is based on this idea.2.1 Basic dd AlgorithmPartition sets into 2 subsets and look out for outcomes, on basis of which youcan decide whether you want to continue search by further partitioning for thatinterference. This dd algorithmic function returns all failure-inducing changes.dd’s time complexity is at worst linear.2.2 Extended dd AlgorithmExtended algorithm dd+, based on 3 cases Found – if testing fails, Interference- if testing in subset and it’s compliment passes and Preference – if testing isunresolved, we repeat the process with 2n subsets with optimized actions forthose 3 cases. dd+ nds a minimal set of failure-inducing changes and all changesthat are safe and not failure-inducing are guaranteed to be excluded.2.3 Avoid InconsistencyGrouping Related Changes: It is useful to group changes together whicha ect any speci c function (Syntactic criteria) or that appeared at a samedate (Process criteria).Predicting Test Outcomes: If observation states that speci c con gurationswill be inconsistent, we can predict their test outcomes as unresolved insteadof carrying out that test itself. Predicting the outcomes are especially usefulif we can impose an ordering on the changes. Moreover, this makes dd+algorithm work like binary search algorithm.2.4 Conclusions and Future WorkIt is recommended that delta debugging be an integrated part of regressiontesting. Every time a regression test fails, a delta debugging program shouldbe started to the rescue. future work of this research paper will concentrate onavoiding inconsistencies by exploiting domain knowledge. Slice of the programwhere program execution may be altered by applying the change, determineforward slices then group changes by the common nodes containing respectiveslices. Also further studies will validate intertwining of changes to constructioncommands, system models, and actual source code must be handled, possibly bymultiversion system models

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