Method and system for identifying errors in code

• US 8,549,359 B2
• Filed: 05/11/2011
• Issued: 10/01/2013
• Est. Priority Date: 05/12/2010
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Zheng, Zuye
• Primary Examiner(s): PATEL, KAMINI B
• Application Number:
  US13/105,849
• Publication Number:
  US 20110283163A1
• Time in Patent Office: 874 Days
• Field of Search: 714/38.11
• US Class Current: 714/38.11
• CPC Class Codes: G06F 11/3612 by runtime analysis performance monitoring G06F11/3466
• CPC Class Codes: G06F 11/362 Software debugging

Abstract

A method for identifying errors in code is provided. The method may include rebuilding object dependencies from a heap dump, calculating memory usage of each object, identifying top consumers of memory by object class, analyzing how much memory each class consumes with respect to how much other classes consume, building a corpus of data that may be used in a progressive machine learning algorithm, and identifying suspect classes. Additionally, the suspect classes and the memory usage statistics of the suspect classes may then be used as an identifying signature of the associated out of memory error. The identifying signature of the associated out of memory error may then be used to compare with the signatures of other out of memory occurrences for identifying duplicate error occurrences.

Claims

1. A method for identifying errors in software code in a multi-tenant environment comprising:

rebuilding, at a host system, object dependencies from a current heap dump;

the host system including a set of one or more processors and a memory system including one or more computer readable media, the host system being part of the multitenant environment;

calculating, by the set of one or more processors memory usage statistics of each of a group of objects that contributed to the current heap dump;

identifying, by the host system, top consumers of memory by object of the current heap dump;

determining, by the host system, information about memory usage for each of the top consumers including at least analyzing how much memory each of the top consumers consumes with respect to how much other top consumers consume;

searching, by the host system, prior heap dumps for heap dumps that are similar to the current heap dump;

if a prior heap dump is found that is similar, grouping, by the host system, the prior heap dump together with the current heap dump in a set of similar heap dumps, and associating information about the set of heap dumps with the current heap dump;

computing, by the host system, a suspect score for each of the top consumers based on information about the set, if a prior similar heap was found for the top consumer, and computing the suspect score for each of the top consumers based on the information about memory usage if no prior similar heap dump was found; and

determining, by the host system, whether each of the top consumers is likely to have caused memory issues based on the suspect score.

2. The method of claim 1, further comprising:

building a corpus of data for identifying suspect classes that are responsible for out of memory errors; and

identifying a signature of an associated out of memory error based on at least the suspect classes and the memory usage statistics.

3. The method of claim 1, where the calculating memory usage statistics further comprises determining a shallow heap, and

determining retained heap usages by traversing through dependency trees of the heap dump.

4. The method of claim 3, the determining of the shallow heap includes at least determining

primitive values and sizes of the pointers to other objects.

5. The method of claim 3, where the shallow and retained heaps are grouped together by class to calculate the shallow and retained heaps of entire classes along with the number of objects created for each class;

and

where each class is analyzed for memory leaks or excessive memory usage.

6. The method of claim 1, where the object classes related to an application are sorted by retained heap size into a list;

where a set of top memory consumers, which are a subset of the object classes on the list, which does not include all of the objects classes on the list are used in building a corpus of data.

7. The method of claim 1,

where

the suspect score is based on a mean and standard deviation of a shallow heap size and a retained heap size,

a number representing how many objects are referenced by the current class, and

an occurrence index value representing a relative size of the retained heap of the current class of the top consumers with respect to other top consumers, the score being computed based on all heap dumps.

8. The method of claim 1, further comprising comparing a signature of the current heap dump associated with an out of memory error with a series of signatures of other heap dumps associated with other out of memory occurrences;

determining whether the current heap dump is a duplicate or an earlier occurrence.

9. The method of claim 8, further comprising:

computing, based on the comparing of the signatures, a duplication score that is indicative of a probability that the current heap dump is a duplicate of a prior heap dump;

if no occurrence with a similar signature is found then the out of memory error is not considered a duplicate, the computing of the suspect score is performed;

if a similar occurrence is found, including the current heap dump in a set of duplicates of the current heap dump;

computing averages and standard deviations of sizes of shallow and retained heaps of the top consuming classes; and

computing the suspect score for the set with the new heap dump, by the set of one or more processors.

10. A non-transitory machine-readable medium carrying one or more instructions for identifying errors in software code in a system, which when executed cause a method to be carried out, the method comprising:

rebuilding, at a host system, object dependencies from a current heap dump;

the host system including a set of one or more processors and a memory system including one or more computer readable media, the host system being part of the multitenant environment;

calculating memory usage statistics of each of a group of objects that contributed to the current heap dump;

identifying, by the host system, top consumers of memory by object of the current heap dump;

determining, by the host system, information about memory usage for each of the top consumers including at least analyzing how much memory each of the top consumers consumes with respect to how much other top consumers consume;

searching, by the host system, prior heap dumps for heap dumps that are similar to the current heap dump;

if a prior heap dump is found that is similar, grouping, by the host system, the prior heap dump together with the current heap dump in a set of similar heap dumps, and associating information about the set of heap dumps with the current heap dump;

computing, by the host system, a suspect score for each of the top consumers based on information about the set, if a prior similar heap was found for the top consumer, and computing the suspect score for each of the top consumers based on the information about memory usage if no prior similar heap dump was found;

determining, by the host system, whether each of the top consumers is likely to have caused memory issues based on the suspect score.

11. A computer network configured for identifying errors in software code a system, the computer network comprising:

a plurality of clusters of servers each having a processor system including at least one processor; and

a memory system including a machine readable medium having stored thereon one or more sequences of instructions which, when executed, cause a method to be carried out, the method comprising;

rebuilding, at a host system, object dependencies from a current heap dump;

the host system including a set of one or more processors and a memory system including one or more computer readable media, the host system being part of the multitenant environment;

calculating, by the set of one or more processors, memory usage statistics of each of a group of objects that contributed to the current heap dump;

identifying, by the host system, top consumers of memory by object of the current heap dump;

determining, by the host system, information about memory usage for each of the top consumers including at least analyzing how much memory each of the top consumers consumes with respect to how much other top consumers consume;

searching, by the host system, prior heap dumps for heap dumps that are similar to the current heap dump;

if a prior heap dump is found that is similar, grouping, by the host system, the prior heap dump together with the current heap dump in a set of similar heap dumps, and associating information about the set of heap dumps with the current heap dump;

computing, by the host system, a suspect score for each of the top consumers based on information about the set, if a prior similar heap was found for the top consumer, and computing the suspect score for each of the top consumers based on the information about memory usage if no prior similar heap dump was found;

determining, by the host system, whether each of the top consumers is likely to have caused memory issues based on the suspect score.