Abstract:
A method for diagnosing software crashes includes retrieving a stack-trace from at least one of a new problem report, updated problem report, and authorized analysis report from a repository. A vector is automatically created from the retrieved stack-trace using the function name and associating the resultant vector with the problem report and authorized analysis reports. Vector space modeling is used to calculate the angles between the resultant vectors to determine similarities. Similar problem reports and authorized analysis reports are grouped into similar sets using a maximal cliques process. New software crashes are automatically diagnosed by extracting the stack-trace from a new problem report of the new software crash, and selecting a potential solution by searching the grouped problem reports and authorized analysis reports for a stack-trace similar to the new stack-trace.

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to the field of operating system (OS) maintenance and service, and more particularly to improved diagnosis of operating system or application software crashes. 
     BACKGROUND OF THE INVENTION 
     Operating system (OS) crashes can result in significant monetary or operational losses in enterprise businesses and financial institutions. Operational losses may include the loss of all application services, data services, and the entire delivery chain of products and/or services provided by a business. When an OS or an application crash occurs, diagnostic data is needed to help identify the root cause of the problem, and to help find a solution. Technical support personnel and developers use stack-trace files to manually determine where the exception occurred for the failing thread/process. A stack-trace, in computing, is a report of the active stack frames at a certain point during the execution of a computer program. In other words, it is the list of function calls that the current thread/process was executing when an exception occurs. A thread is an execution stream within a process with its own stack, local variables, and program counter. There may be one or more execution streams in a process. Typically, technical support personnel search for similar known problems in a prior problems database for applicable solutions and developers try to manually determine the faulting function name and offset of the exception by analyzing registers, memory content, checking for reasonable outcomes during execution, and backtracking the stack-trace while reading the source code for each function in the stack-trace. A register is a small amount of high speed memory available as part of a Central Processing Unit (CPU) or a hardware-thread of a CPU, designed to speed up its operations by providing quick access to commonly used values. 
     SUMMARY 
     Embodiments in accordance with the present invention disclose a method, computer program product, and system for diagnosing software crashes. A method includes retrieving a stack-trace from at least one of a new problem report, updated problem report, and authorized analysis report from a repository. A vector is automatically created from the retrieved stack-trace using the function name and associating the resultant vector with the problem report and authorized analysis reports. Vector space modeling is used to calculate the angles between the resultant vectors to determine similarities. Similar problem reports and authorized analysis reports are grouped into similar sets using a maximal cliques process. New software crashes are automatically diagnosed by extracting the stack-trace from a new problem report of the new software crash, and selecting a potential solution by searching the grouped problem reports and authorized analysis reports for a stack-trace similar to the new stack-trace. 
     A computer program product for diagnosing software crashes includes one or more computer-readable storage media and program instructions stored on at least one of the one or more storage media, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to perform a method that includes retrieving a stack-trace from at least one of a new problem report, updated problem report, and authorized analysis report from a repository. A vector is automatically created from the retrieved stack-trace using the function name and associating the resultant vector with the problem report and authorized analysis reports. Vector space modeling is used to calculate the angles between the resultant vectors to determine similarities. Similar problem reports and authorized analysis reports are grouped into similar sets using a maximal cliques process. New software crashes are automatically diagnosed by extracting the stack-trace from a new problem report of the new software crash, and selecting a potential solution by searching the grouped problem reports and authorized analysis reports for a stack-trace similar to the new stack-trace. 
     A computer system for diagnosing software crashes includes one or more computer processors and one or more computer readable storage media. Program instructions are stored on the computer readable storage media, wherein execution of the program instructions by the one or more processors of the computer system causes the one or more processors to perform a method that includes retrieving a stack-trace from at least one of a new problem report, updated problem report, and authorized analysis report from a repository. A vector is automatically created from the retrieved stack-trace using the function name and associating the resultant vector with the problem report and authorized analysis reports. Vector space modeling is used to calculate the angles between the resultant vectors to determine similarities. Similar problem reports and authorized analysis reports are grouped into similar sets using a maximal cliques process. New software crashes are automatically diagnosed by extracting the stack-trace from a new problem report of the new software crash, and selecting a potential solution by searching the grouped problem reports and authorized analysis reports for a stack-trace similar to the new stack-trace. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  is a functional block diagram illustrating an OS diagnostic environment, in an embodiment in accordance with the present invention. 
         FIG. 2  is a flowchart illustrating operational steps for batch processing of new or modified problem reports and authorized analysis reports of crashed software instances, in an embodiment in accordance with the present invention. 
         FIG. 3  is a flowchart illustrating operational steps for real-time processing of a new problem report of a software crash with a stack-trace, in an embodiment in accordance with the present invention. 
         FIG. 4  is a functional block diagram of a computer system, in an embodiment in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments in accordance with the present invention recognize that software crashes may be quickly diagnosed by grouping existing software crashes into groups called cliques of similar problems based on their stack-traces, then identifying a similar problem by searching those cliques of stack-traces when a new software crash occurs. OS crashes can result in significant monetary and operational losses in enterprise businesses and financial institutions. An OS crash results in the loss of all application services and/or data services. IT disruptions can affect the entire delivery chain of products and/or services provided by a business. OS crashes are reported to OS-vendors in the form of crash reports. Crash reports contain the crash details and often include data such as stack-traces, type of crash, the program function causing the crash, the OS version and release. An OS, e.g., AIX, UNIX, or Linux™, kernel crash dump file containing a stack-trace, and Java™ JVM snap trace are examples of files that can be used to determine the cause of the crash. When an OS crash is reported to an OS-vendor, technical support personnel are required to manually search for similar incident on various large databases. Quickly diagnosing an OS crash to find its root cause and finding known solutions that have been applied to similar problem instances, in order to reduce downtimes is critical in any enterprise business environment. 
     Embodiments in accordance with the present invention will now be described in detail with reference to the Figures.  FIG. 1  is a functional block diagram, generally depicted by the numeral  100 , illustrating an OS diagnostic environment, in an embodiment in accordance with the present invention. Analytics server  102  includes Random Access Memory (RAM)  104 , central processing unit  106 , and persistent storage  108 . Persistent storage  108  may, for example, be a hard disk drive. Problem report (PR) analyzer  110  and analytic repository  112  are stored in persistent storage  108 , which includes operating system software as well as software that enables analytics server  102  to communicate with problem report database  116 , patch support database  128 , and customer knowledgebase database  138  over a data connection on network  114 . In other embodiments, problem report database  116 , patch support database, and customer knowledgebase database  138  may be repositories or other storage devices capable of storing data such as crash reports, patches, and knowledgebase repositories e.g., IBM® Post Sales Database (PSdb), IBM® Support Fix Central Database, and IBM® Enhanced Customer Repository (ECuRep). In other embodiments, PR analyzer  110  and analytic repository  112  could be stored in separate server computer systems. 
     In  FIG. 1 , network  114  is shown as the interconnecting fabric between analytics server  102 , problem report database  116 , patch support database  128 , and customer knowledgebase database  138 . In practice, the connection may be any viable data transport network, such as, for example, a LAN or WAN. Network  114  can be, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and include wired, wireless, or fiber optic connections. In general, network  114  can be any combination of connections and protocols that will support communications between analytics server  102 , problem report database  116 , patch support database  128 , and customer knowledgebase database  138  in accordance with a desired embodiment of the invention. 
     Problem report database  116  also contains RAM  118 , central processing unit  120 , and persistent storage  122  such as a hard disk drive. Problem report (PR) repository  124  and authorized analysis report (AAR) database  126  is stored in persistent storage  122 , including operating system software as well as software that enables problem report database  116  to communicate with analytics server  102  over a data connection on network  114 . A PR may be, for example, a customer problem management record, which is a document used to manage any technical product issue that a customer reports to a software vendor. Once created, the PR will be assigned a unique number which is communicated in real-time to the customer. Each time a software vendor support analyst updates a PR, the customer will receive an e-mail notifying them of the update. When necessary, the customer should respond with any information, data, or further questions they may have related to the reported issue. 
     An AAR is a formal report from the software or OS-vendor development-team of a problem caused by a suspected defect in a current release of a software/OS-vendor program. If the software/OS-vendor development-team is able to confirm the existence of the defect, they will update the AAR with any known workarounds. Additionally, an indication of a future release, if any, of the software/OS-vendor program targeting a formal fix for the defect as well as a patch or Program Temporary Fix (PTF), if planned, may be included. The AAR will then be published and visible to supported customers. In one embodiment, problem report database  116 , typically a server with large amounts of storage, is capable of communicating with analytics server  102  via network  114 . In other embodiments, problem report database  116  may consist of multiple server computer system repositories capable of communicating with analytics server  102  via network  114 . 
     Patch support database  128  also contains RAM  130 , central processing unit (CPU)  132 , and persistent storage  134  such as a hard disk drive. Patch repository  136  is stored in persistent storage  134 , including operating system software as well as software that enables patch support database  128  to communicate with analytics server  102  over a data connection on network  114 . In other embodiments, patch repository  136  may be used to store computer program fixes in the form of executable files or in the form of source code consisting of textual differences between two source code files. In other embodiments, patch support database  128  may consist of multiple server computer repositories capable of communicating with analytics server  102  via network  114 . 
     Customer knowledgebase database  138  also contains RAM  140 , central processing unit  142 , and persistent storage  144  such as a hard disk drive. Customer knowledge repository  146  is stored in persistent storage  144 , including operating system software as well as software that enables customer knowledgebase database  138  to communicate with analytics server  102  over a data connection on network  114 . In another embodiment, customer knowledgebase database  138  may consist of multiple server computer repositories, containing large amounts of storage, capable of communicating with analytics server  102  via network  114 . 
       FIG. 2  is a flowchart, generally depicted by the numeral  200 , illustrating operational steps for batch processing of new or modified problem reports and authorized analysis reports of crashed software instances, in an embodiment in accordance with the present invention. In one embodiment, this process is performed during off-peak hours due to the large amounts of data being processed and transferred to analytics server  102 . In other embodiments, this process could be performed at any time. In other embodiments, PR analyzer  110  may be used to diagnose application program dumps for software programs that abnormally end or crash. 
     PR analyzer  110  checks problem report database  116  for new or updated PRs in PR repository  124  and AARs in AAR DB  126  as indicated in decision  202 . If new or updated PRs or AARs exist in PR repository  124  or AAR DB  126  (“yes” branch, decision  202 ), PR analyzer  110  retrieves the new or updated PRs or AARs from PR repository  124  and AAR DB  126  as shown in step  204 . If there are no new or updated PRs in PR repository  124  or AARs in AAR DB  126  (“no” branch, decision  202 ), PR analyzer  110  bypasses steps  204  through decision  216 . In step  206 , PR analyzer  110  parses all new PR and AAR files in PR repository  124  and AAR DB  126  to extract the associated stack-traces. In step  208 , PR analyzer  110  then creates vectors from the stack-traces using the faulting function name and offset of the exception that caused the crash, and stores those vectors in analytics server  102 . A vector is a mathematical structure formed by a collection of elements, which may be added together and multiplied by numbers. PR analyzer  110  uses each of the program function-names as a distinct dimension and the offset within the associated function as the length of the vector. 
     PR analyzer  110  applies a text mining technique called Vector Space Modeling to each PR or AAR to find similarity between the set of vectors stored in the analytics server as shown in step  210 . Vector Space Modeling is an algebraic model for representing text documents, or any object in general, as vectors of identifiers. Every distinct term in a document constitutes a dimension. For each document, the frequency of that term is the length of the vector in that dimension, i.e., each document is represented as a multi-dimensional vector. In an embodiment in accordance with the present invention, the stack traces, rather than term frequencies, are used to create the vectors. The angle between two vectors is then computed. Two vectors are defined as similar if the angle between the two vectors is below a user-specified value. For example, one can define two PRs or AARs to be similar when the angle between the corresponding two vectors is less than one degree. A difference angle threshold of one degree is an empirically derived value that has been determined to provide acceptable performance, although it is conceivable that other difference angles may also provide acceptable performance, or even improved performance in some contexts. 
     In step  212 , PR analyzer  110  then groups similar PRs and AARs together in cliques using the Maximal Cliques process in Undirected Graphs. In the mathematical field of graph theory, a clique in an undirected graph is a subset of its vertices such that every two vertices in the subset are connected by an edge. Vertices are also called nodes or points. A maximal clique is a clique that does not exist exclusively within the vertex set of a larger clique. PR analyzer  110  classifies a pair of PRs or AARs as similar if the angle between the PRs or AARs is less than or equal to a user-specified and fixed threshold. Angles greater than the user-specified fixed threshold are classified as dissimilar by PR analyzer  110  and another pair is selected. When the vertices of two PRs or AARs are classified as similar, an edge is added by PR analyzer  110  to connect the PRs or AARs. Every maximal clique represents a maximally-connected set of vertices, i.e., similar PRs or AARs. Each PR and AAR is represented by a vertex. If two PRs or AARs are determined to be similar by the above described vector space modeling, PR analyzer  110  connects the PRs or AARs by an edge to form a graph. PR analyzer  110  then extracts the separate component sub-graphs from the graph and the maximal clique is determined. Each maximal clique represents a set of similar PRs and AARs. 
     In step  214 , PR analyzer  110  stores the cliques into analytic repository  112 . PR analyzer  110  then checks problem report database  116  for new or updated PRs in PR repository  124  and AARs in AAR DB  126  again, as seen in decision  216 . If additional new or updated PRs or AARs exist in the queue (“yes” branch, decision  216 ), PR analyzer  110  repeats the process and returns to step  202 . If there are no additional new or updated PRs in PR repository  124  or AARs in AAR DB  126 , (“no” branch, decision  216 ), PR analyzer  110  continues on to step  218 . In step  218 , PR analyzer  110  schedules the next user determined scan for new or updated PRs in PR repository  124  and AARs in AAR DB  126  on problem report database  116 . 
       FIG. 3  is a flowchart, generally depicted by the numeral  300 , illustrating operational steps for real-time processing of a new problem report of a software crash with a stack-trace, in an embodiment in accordance with the present invention. Problem report database  116  receives a new PR in PR repository  124  or AAR in AAR DB  126  as depicted in step  302  and passes the information to PR analyzer  110 . PR analyzer  110  then parses the new PR or AAR to extract the stack-trace. In step  304 , PR analyzer  110  chooses a clique from the list of cliques in the analytic repository  112  and compares the vector of the new stack-trace to existing vector of one of the stack-traces in the clique as depicted in step  306 . PR analyzer  110  then checks to see if the stack-traces are similar, decision  308 . If the stack-traces are not similar (“no” branch, decision  308 ), PR analyzer  110  determines if all cliques in analytic repository  112  have been searched, as seen in decision  310 . If all cliques were searched (“yes” branch, decision  310 ), PR analyzer  110  reports no similar PR or AAR were found to system administrators via email or other digital forms of communication, as seen in step  314 . If all cliques in analytic repository  112  were not searched (“no” branch, decision  310 ), PR analyzer  110  goes on to the next clique, as seen in step  312 . 
     If the stack-traces are similar (“yes” branch, decision  308 ), PR analyzer  110  compares the stack-traces of all the PRs and AARs in that clique as seen in step  316 . In decision  318 , PR analyzer  110  determines if all the PR and AAR stack-traces are similar to the new stack-trace. If not (“no” branch, decision  318 ), PR analyzer  110  moves to the next clique as seen in step  320 . If all PRs and AARs match the new stack-trace (“yes” branch, decision  318 ), PR analyzer  110  checks to see if there are any AARs in the clique as seen in decision  322 . If no AARs exist in the clique (“no” branch, decision  322 ), PR analyzer  110  reports all similar PRs in the clique to system administrators via email or other digital forms of communication and logs the results to customer knowledge repository  146  as shown in step  324 . If there are any AARs in the clique (“yes” branch, decision  322 ), PR analyzer  110  then determines if there are any patches specified in the AAR and stored in patch repository  136  as illustrated in decision  326 . If there are no patches specified in the AAR (“no” branch, decision  326 ), PR analyzer  110  reports all the similar AARs in the clique to system administrators via email or other digital forms of communication and logs the results to customer knowledge repository  146  as seen in step  330 . If patches are specified in the AAR (“yes” branch, decision  326 ), PR analyzer  110  reports all the patches contained in patch repository  136 , with each alongside its AAR, to system administrators via email or other digital forms of communication and logs the results to customer knowledge repository  146  as shown in step  328 . 
       FIG. 4  depicts a block diagram, generally depicted by the numeral  400 , of components of analytics server  102  in an embodiment in accordance with the present invention. It should be appreciated that  FIG. 4  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
     Analytics server  102  includes communications fabric  402 , which provides communications between computer processor(s)  404 , memory  406 , persistent storage  408 , communications unit  410 , and input/output (I/O) interface(s)  412 . Communications fabric  402  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  402  can be implemented with one or more buses. 
     Memory  406  and persistent storage  408  are computer readable storage media. In this embodiment, memory  406  includes random access memory (RAM)  414  and cache memory  416 . In general, memory  406  can include any suitable volatile or non-volatile computer readable storage media. 
     PR analyzer  110  and analytic repository  112  are stored in persistent storage  408  for execution and/or access by one or more of the respective computer processors  404  via one or more memories of memory  406 . In this embodiment, persistent storage  408  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  408  can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  408  may also be removable. For example, a removable hard drive may be used for persistent storage  408 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  408 . 
     Communications unit  410 , in these examples, provides for communications with other data processing systems or devices, including resources of network  114  and problem report database  116 , patch support database  128 , and customer knowledgebase database  138 . In these examples, communications unit  410  includes one or more network interface cards. Communications unit  410  may provide communications through the use of either or both physical and wireless communications links. PR analyzer  110  and analytic repository  112  may be downloaded to persistent storage  408  through communications unit  410 . 
     I/O interface(s)  412  allows for input and output of data with other devices that may be connected to analytics Server  102 . For example, I/O interface  412  may provide a connection to external devices  418  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  418  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., PR analyzer  110  and analytic repository  112 , can be stored on such portable computer readable storage media and can be loaded onto persistent storage  408  via I/O interface(s)  412 . I/O interface(s)  412  also connect to a display  420 . 
     Display  420  provides a mechanism to display data to a user and may be, for example, a computer monitor. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.