Author: Chao Liu, Xiangyu Zhang Presented by Wenbin Li

 Automated failure reporting is widely used  failure indexing: which asks how to identify all failures due to the same fault ◦ Failure Prioritization ◦ Duplicate Failure Removal ◦ Patch Suggestion

 Crashing failure: ◦ Incurred by memory bugs ◦ Nearly one-to-one mapping  Noncrashing failure: ◦ Caused by semantic bugs ◦ A hard problem

 T-Proximity ◦ Weakness: failures with similar behaviors rather than the same fault are indexed together  R-Proximity ◦ Weakness: Use passing execution, which may violate the privacy of users.

 identify a subset of program statements that are involved in producing a program failure.  execution instances: it is used to distinguish different execution of the same statement s. Suppose s is executed n times, we use s1; s2; …; sn to denote the n execution instances.  Data slice and full slice will be used in this study.

 For example:  E i = (10, 30, 40)  E j = (10, 20, 30, 40)  D(e i, e j ) = 1- ¾ = 1/4

 To prepare for the failure indexing, we should do the following: 1. “due to” function 2. partitions the set of failuresX into m mutually exclusive and collectively exhaustive sets 3. define G(x i ) includes all the failures due to the same fault as x i

 The distance function D quantifies how failures are close to each other based on the similarity between their corresponding failure signature  Use a pair-wise distance matrix M(Ǿ;D), which is called the proximity matrix, to calculated for the given set of n failures

 Two Concepts: ◦ Cohesion:  To what extent failures in the same group are close to each other; ◦ Separation:  To what extent failures in different groups are separated from each other.  Both Cohesion and Separation should be HIGH

 Problem: objects reside in a very high- dimensional space. Each failure is in a space of hundreds of dimensions because a typical slice contains hundreds of statements.  Method: multidimensional scaling (MDS) techniques. The techniques re-arrange them in a specific way in a much lower (usually 2) dimensional space.

 The two faults are manually seeded.  hand-crafted test inputs are used.  Metrics may not be accurate enough

 The approach is more fault-relevant than the method used by T-PROXIMITY.  The approach eliminates the need of passing executions, which is used by R-PROXIMITY.

 It is hard to define data slices, considering how many codes a software may contain.  The paper just provides some special example, which is not enough to prove the effectiveness of the approach.