Abstract:
A dispersed storage network facilitates isolating the introduction of software defects in dispersed storage units. A search strategy is employed whereby after identifying a test failure in a current version of the memory software code, a code version since a previous successfully tested version is identified. An interim version that represents the point at which approximately one half the changes were introduced is then tested. When there is a test failure, the next interim version selected for testing represents the point at which approximately one half the changes were introduced between the first interim version tested and the current version. If no failure, a next interim version is tested that represents the point at which approximately one half the changes were introduced between the previous successfully tested version and the first interim version tested.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. §119(e) to U.S. Provisional Application No. 62/314,839, filed 29 Mar. 2016, entitled “Processing an Encoded Data Slice in a Dispersed Storage Network,” which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility patent application for all purposes. 
     
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
       [0002]    Not applicable. 
       INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC 
       [0003]    Not applicable. 
       BACKGROUND OF THE INVENTION 
     Technical Field of the Invention 
       [0004]    This invention relates generally to computer networks, and more particularly isolating the introduction of software defects in a dispersed storage network. 
       Description of Related Art 
       [0005]    Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure. 
         [0006]    As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers. 
         [0007]    In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc. on a remote storage system. The remote storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage. 
         [0008]    In a RAID system, a RAID controller adds parity data to the original data before storing it across an array of disks. The parity data is calculated from the original data such that the failure of a single disk typically will not result in the loss of the original data. While RAID systems can address certain memory device failures, these systems may suffer from effectiveness, efficiency and security issues. For instance, as more disks are added to the array, the probability of a disk failure rises, which may increase maintenance costs. When a disk fails, for example, it needs to be manually replaced before another disk(s) fails and the data stored in the RAID system is lost. To reduce the risk of data loss, data on a RAID device is often copied to one or more other RAID devices. While this may reduce the possibility of data loss, it also raises security issues since multiple copies of data may be available, thereby increasing the chances of unauthorized access. In addition, co-location of some RAID devices may result in a risk of a complete data loss in the event of a natural disaster, fire, power surge/outage, etc. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
         [0009]      FIG. 1  is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN) in accordance with the present disclosure; 
           [0010]      FIG. 2  is a schematic block diagram of an embodiment of a computing core in accordance with the present disclosure; 
           [0011]      FIG. 3  is a schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present disclosure; 
           [0012]      FIG. 4  is a schematic block diagram of a generic example of an error encoding function in accordance with the present disclosure; 
           [0013]      FIG. 5  is a schematic block diagram of a specific example of an error encoding function in accordance with the present disclosure; 
           [0014]      FIG. 6  is a schematic block diagram of an example of slice naming information for an encoded data slice (EDS) in accordance with the present disclosure; 
           [0015]      FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present disclosure; 
           [0016]      FIG. 8  is a schematic block diagram of a generic example of an error decoding function in accordance with the present disclosure; 
           [0017]      FIG. 9  is a schematic block diagram of an example of a dispersed storage network in accordance with the present disclosure; 
           [0018]      FIG. 10A  is a schematic diagram of another embodiment of a dispersed storage network in accordance with the present disclosure; 
           [0019]      FIG. 10B  is a schematic diagram of another embodiment of a dispersed storage network in accordance with the present disclosure; and 
           [0020]      FIG. 11  is a block diagram illustrating differing versions of memory software code in accordance with the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0021]      FIG. 1  is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN)  10  that includes a plurality of computing devices  12 - 16 , a managing unit  18 , an integrity processing unit  20 , and a DSN memory  22 . The components of the DSN  10  are coupled to a network  24 , which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN). 
         [0022]    The DSN memory  22  includes a plurality of storage units  36  that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSN memory  22  includes eight storage units  36 , each storage unit is located at a different site. As another example, if the DSN memory  22  includes eight storage units  36 , all eight storage units are located at the same site. As yet another example, if the DSN memory  22  includes eight storage units  36 , a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that a DSN memory  22  may include more or less than eight storage units  36 . 
         [0023]    Each of the computing devices  12 - 16 , the managing unit  18 , and the integrity processing unit  20  include a computing core  26 , and network interfaces  30 - 33  which can be part of or external to computing core  26 . Computing devices  12 - 16  may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. Note that each of the managing unit  18  and the integrity processing unit  20  may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices  12 - 16  and/or into one or more of the storage units  36 . 
         [0024]    Each interface  30 ,  32 , and  33  includes software and hardware to support one or more communication links via the network  24  indirectly and/or directly. For example, interface  30  supports a communication link (e.g., wired, wireless, direct, via a LAN, via the network  24 , etc.) between computing devices  14  and  16 . As another example, interface  32  supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network  24 ) between computing devices  12  and  16  and the DSN memory  22 . As yet another example, interface  33  supports a communication link for each of the managing unit  18  and the integrity processing unit  20  to the network  24 . 
         [0025]    Computing devices  12  and  16  include a dispersed storage (DS) client module  34 , which enables the computing device to dispersed storage error encode and decode data (e.g., data object  40 ) as subsequently described with reference to one or more of  FIGS. 3-8 . In this example embodiment, computing device  16  functions as a dispersed storage processing agent for computing device  14 . In this role, computing device  16  dispersed storage error encodes and decodes data on behalf of computing device  14 . With the use of dispersed storage error encoding and decoding, the DSN  10  is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, the DSN  10  stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing the data). 
         [0026]    In operation, the managing unit  18  performs DS management services. For example, the managing unit  18  establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices  12 - 16  individually or as part of a group of user devices. As a specific example, the managing unit  18  coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within the DSN memory  22  for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managing unit  18  facilitates storage of DS error encoding parameters for each vault by updating registry information of the DSN  10 , where the registry information may be stored in the DSN memory  22 , a computing device  12 - 16 , the managing unit  18 , and/or the integrity processing unit  20 . The DS error encoding parameters (e.g., or dispersed storage error coding parameters) include data segmenting information (e.g., how many segments data (e.g., a file, a group of files, a data block, etc.) is divided into), segment security information (e.g., per segment encryption, compression, integrity checksum, etc.), error coding information (e.g., pillar width, decode threshold, read threshold, write threshold, etc.), slicing information (e.g., the number of encoded data slices that will be created for each data segment); and slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.). 
         [0027]    The managing unit  18  creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSN memory  22 . The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme. 
         [0028]    The managing unit  18  creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the managing unit  18  tracks the number of times a user accesses a non-public vault and/or public vaults, which can be used to generate per-access billing information. In another instance, the managing unit  18  tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate per-data-amount billing information. 
         [0029]    As another example, the managing unit  18  performs network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, storage units, and/or computing devices with a DS client module  34 ) to/from the DSN  10 , and/or establishing authentication credentials for the storage units  36 . Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the DSN  10 . Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the DSN  10 . 
         [0030]    To support data storage integrity verification within the DSN  10 , the integrity processing unit  20  (and/or other devices in the DSN  10 ) may perform rebuilding of ‘bad’ or missing encoded data slices. At a high level, the integrity processing unit  20  performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSN memory  22 . Retrieved encoded slices are checked for errors due to data corruption, outdated versioning, etc. If a slice includes an error, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slices that are not received and/or not listed may be flagged as missing slices. Bad and/or missing slices may be subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices in order to produce rebuilt slices. A multi-stage decoding process may be employed in certain circumstances to recover data even when the number of valid encoded data slices of a set of encoded data slices is less than a relevant decode threshold number. The rebuilt slices may then be written to DSN memory  22 . Note that the integrity processing unit  20  may be a separate unit as shown, included in DSN memory  22 , included in the computing device  16 , and/or distributed among the storage units  36 . 
         [0031]      FIG. 2  is a schematic block diagram of an embodiment of a computing core  26  that includes a processing module  50 , a memory controller  52 , main memory  54 , a video graphics processing unit  55 , an input/output (TO) controller  56 , a peripheral component interconnect (PCI) interface  58 , an IO interface module  60 , at least one IO device interface module  62 , a read only memory (ROM) basic input output system (BIOS)  64 , and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module  66 , a host bus adapter (HBA) interface module  68 , a network interface module  70 , a flash interface module  72 , a hard drive interface module  74 , and a DSN interface module  76 . 
         [0032]    The DSN interface module  76  functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). The DSN interface module  76  and/or the network interface module  70  may function as one or more of the interface  30 - 33  of  FIG. 1 . Note that the IO device interface module  62  and/or the memory interface modules  66 - 76  may be collectively or individually referred to as IO ports. 
         [0033]      FIG. 3  is a schematic block diagram of an example of dispersed storage error encoding of data. When a computing device  12  or  16  has data to store it disperse storage error encodes the data in accordance with a dispersed storage error encoding process based on dispersed storage error encoding parameters. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematic encoding, on-line codes, etc.), a data segmenting protocol (e.g., data segment size, fixed, variable, etc.), and per data segment encoding values. The per data segment encoding values include a total, or pillar width, number (T) of encoded data slices per encoding of a data segment (i.e., in a set of encoded data slices); a decode threshold number (D) of encoded data slices of a set of encoded data slices that are needed to recover the data segment; a read threshold number (R) of encoded data slices to indicate a number of encoded data slices per set to be read from storage for decoding of the data segment; and/or a write threshold number (W) to indicate a number of encoded data slices per set that must be accurately stored before the encoded data segment is deemed to have been properly stored. The dispersed storage error encoding parameters may further include slicing information (e.g., the number of encoded data slices that will be created for each data segment) and/or slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.). 
         [0034]    In the present example, Cauchy Reed-Solomon has been selected as the encoding function (a generic example is shown in  FIG. 4  and a specific example is shown in  FIG. 5 ); the data segmenting protocol is to divide the data object into fixed sized data segments; and the per data segment encoding values include: a pillar width of 5, a decode threshold of 3, a read threshold of 4, and a write threshold of 4. In accordance with the data segmenting protocol, the computing device  12  or  16  divides the data (e.g., a file (e.g., text, video, audio, etc.), a data object, or other data arrangement) into a plurality of fixed sized data segments (e.g., 1 through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more). The number of data segments created is dependent of the size of the data and the data segmenting protocol. 
         [0035]    The computing device  12  or  16  then disperse storage error encodes a data segment using the selected encoding function (e.g., Cauchy Reed-Solomon) to produce a set of encoded data slices.  FIG. 4  illustrates a generic Cauchy Reed-Solomon encoding function, which includes an encoding matrix (EM), a data matrix (DM), and a coded matrix (CM). The size of the encoding matrix (EM) is dependent on the pillar width number (T) and the decode threshold number (D) of selected per data segment encoding values. To produce the data matrix (DM), the data segment is divided into a plurality of data blocks and the data blocks are arranged into D number of rows with Z data blocks per row. Note that Z is a function of the number of data blocks created from the data segment and the decode threshold number (D). The coded matrix is produced by matrix multiplying the data matrix by the encoding matrix. 
         [0036]      FIG. 5  illustrates a specific example of Cauchy Reed-Solomon encoding with a pillar number (T) of five and decode threshold number of three. In this example, a first data segment is divided into twelve data blocks (D1-D12). The coded matrix includes five rows of coded data blocks, where the first row of X11-X14 corresponds to a first encoded data slice (EDS  1 _ 1 ), the second row of X21-X24 corresponds to a second encoded data slice (EDS  2 _ 1 ), the third row of X31-X34 corresponds to a third encoded data slice (EDS  3 _ 1 ), the fourth row of X41-X44 corresponds to a fourth encoded data slice (EDS  4 _ 1 ), and the fifth row of X51-X54 corresponds to a fifth encoded data slice (EDS  5 _ 1 ). Note that the second number of the EDS designation corresponds to the data segment number. In the illustrated example, the value X11=aD1+bD5+cD9, X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12. 
         [0037]    Returning to the discussion of  FIG. 3 , the computing device also creates a slice name (SN) for each encoded data slice (EDS) in the set of encoded data slices. A typical format for a slice name  80  is shown in  FIG. 6 . As shown, the slice name (SN)  80  includes a pillar number of the encoded data slice (e.g., one of 1-T), a data segment number (e.g., one of 1-Y), a vault identifier (ID), a data object identifier (ID), and may further include revision level information of the encoded data slices. The slice name functions as at least part of a DSN address for the encoded data slice for storage and retrieval from the DSN memory  22 . 
         [0038]    As a result of encoding, the computing device  12  or  16  produces a plurality of sets of encoded data slices, which are provided with their respective slice names to the storage units for storage. As shown, the first set of encoded data slices includes EDS  1 _ 1  through EDS  5 _ 1  and the first set of slice names includes SN  1 _ 1  through SN  5 _ 1  and the last set of encoded data slices includes EDS  1 _Y through EDS  5 _Y and the last set of slice names includes SN  1 _Y through SN  5 _Y. 
         [0039]      FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of a data object that was dispersed storage error encoded and stored in the example of  FIG. 4 . In this example, the computing device  12  or  16  retrieves from the storage units at least the decode threshold number of encoded data slices per data segment. As a specific example, the computing device retrieves a read threshold number of encoded data slices. 
         [0040]    In order to recover a data segment from a decode threshold number of encoded data slices, the computing device uses a decoding function as shown in  FIG. 8 . As shown, the decoding function is essentially an inverse of the encoding function of  FIG. 4 . The coded matrix includes a decode threshold number of rows (e.g., three in this example) and the decoding matrix in an inversion of the encoding matrix that includes the corresponding rows of the coded matrix. For example, if the coded matrix includes rows  1 ,  2 , and  4 , the encoding matrix is reduced to rows  1 ,  2 , and  4 , and then inverted to produce the decoding matrix. 
         [0041]      FIG. 9  is a diagram of an example of a dispersed storage network. The dispersed storage network includes a DS (dispersed storage) client module  34  (which may be in computing devices  12 ,  14  and/or  16  of  FIG. 1 ), a network  24 , and a plurality of DS units  1 - n  that includes two or more DS units  36  of  FIG. 1  (which form at least a portion of DS memory  22  of  FIG. 1 ), a DSN managing unit (not shown—device  18  in  FIG. 1 ), and a DS integrity verification module (not shown—device  20  in  FIG. 1 ). The DS client module  34  includes an outbound DS processing section  81  and an inbound DS processing section  82 . Each of the DS units  1 - n  includes a controller  86 , a processing module  84 , memory  88 , a DT (distributed task) execution module  90 , and a DS client module  34 . 
         [0042]    In an example of operation, the DS client module  34  receives data  92 . The data  92  may be of any size and of any content, where, due to the size (e.g., greater than a few Terabytes), the content (e.g., secure data, etc.), and/or concerns over security and loss of data, distributed storage of the data is desired. For example, the data  92  may be one or more digital books, a copy of a company&#39;s emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes). 
         [0000]    Within the DS client module  34 , the outbound DS processing section  81  receives the data  92 . The outbound DS processing section  81  processes the data  92  to produce slice groupings  96 . As an example of such processing, the outbound DS processing section  81  partitions the data  92  into a plurality of data partitions. For each data partition, the outbound DS processing section  81  dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into a slice grouping  96 . 
         [0043]    The outbound DS processing section  81  then sends, via the network  24 , the slice groupings  96  to the DS units  1 - n  of the DSN memory  22  of  FIG. 1 . For example, the outbound DS processing section  81  sends slice group to DS storage unit  1 . As another example, the outbound DS processing section  81  sends slice group #n to DS unit #n. 
         [0044]    In one example of operation, the DS client module  34  requests retrieval of stored data within the memory of the DS units  36 . In this example, the task  94  is retrieve data stored in the DSN memory  22 . Accordingly, and according to one embodiment, the outbound DS processing section  81  converts the task  94  into a plurality of partial tasks  98  and sends the partial tasks  98  to the respective DS storage units  1 - n.    
         [0045]    In response to the partial task  98  of retrieving stored data, a DS storage unit  36  identifies the corresponding encoded data slices and retrieves them. For example, DS unit # 1  receives partial task # 1  and retrieves, in response thereto, retrieved slices # 1 . The DS units  36  send their respective retrieved slices  100  to the inbound DST processing section  82  via the network  24 . 
         [0046]    The inbound DS processing section  82  converts the retrieved slices  100  into data  92 . For example, the inbound DS processing section  82  de-groups the retrieved slices  100  to produce encoded slices per data partition. The inbound DS processing section  82  then DS error decodes the encoded slices per data partition to produce data partitions. The inbound DS processing section  82  de-partitions the data partitions to recapture the data  92 . 
         [0047]    The DSN managing unit  18  of  FIG. 1  may be used to facilitate isolating the introduction of software defects in the storage units or DS units ( 36 ) of  FIGS. 1 and 9 . Further explanations of this process are set out below in conjunction with  FIGS. 10A and 10B . 
         [0048]      FIG. 10A  is a schematic block diagram of another embodiment of a dispersed storage network that includes the computing device  16  of  FIG. 1 , the dispersed storage network (DSN) managing unit  18  of  FIG. 1 , and a set of storage units  1 - n  ( 36 - 1 ,  36 - 2 , . . . ,  36 - n ). Each storage unit may be implemented utilizing the DS unit  36  of  FIGS. 1 and 9 . Hereafter, the set of storage units may be interchangeably referred to as a set of DS units. The DSN functions to identify a software version associated with a software-based operational error. 
         [0049]      FIG. 11  is a block diagram  1100  illustrating differing versions of memory software code in accordance with the present invention. In an example of operation of the identifying of the software version, the DSN managing unit  18  detects the software operational error within the DS units, where a software test perform on a version of software produces an unfavorable result. The detecting includes one or more of interpreting an error message, selecting the software test from a plurality of software tests, facilitating the exercising of the DS unit software in accordance with the selected software test to produce a software test resultant, and indicating the software operational error when the software test resultant compares unfavorably to an expected software test resultant. For example, the DSN managing unit  18  issues a test request(s) (m) to computing device  16  and the set of storage units ( 36 - 1 ,  36 - 2 , . . . ,  36 - n ), receives test responses (m) from units of the DSN, and interprets the test responses (m), where the computing device  16  may issue, via the network  24 , test slices  1 - n  to the set of storage units ( 36 - 1 ,  36 - 2 , . . . ,  36 - n ) and interpret received test slices  1 - n  to produce at least some of the test responses. 
         [0050]    Having detected the software operational error, the DSN managing unit  18  identifies a previous version of software associated with favorable software operation, where the software test performed on the previous version of software produces a favorable result. The identifying includes interpreting a software test record using an identifier of the software test to identify the previous version. Having identified the previous version, the DSN managing unit  18  identifies a sequence of software versions that includes a sequential series of one or more interim software versions that ranges from the previous version software to the version of software (as shown as element  1110  in  FIG. 11 ). The identifying includes one or more of performing a lookup, interpreting a query result, interpreting an error message, and interpreting test records. 
         [0051]    Having identified sequences suffer versions, the DSN managing unit  18  identifies a defect and cause interim software version of the one or more interim software versions utilizing a binary search software test approach. As shown in  FIG. 11 , and will be explained in more detail below, the identifying includes, for a remaining sub-sequence of interim software versions, selecting a middle software version as a next test interim software version, selecting a software test associated with the software operational error and the next test interim software version, loading corresponding units (e.g. computing device  16  and/or DS units  36 ) of the DSN with the next interim software version, performing the selected software test on the DS unit software in question to produce a next test interim software version test result, indicating to move to an older interim software version when the test result is unfavorable or indicating to move to a newer interim software version when the test result is favorable, repeating the binary search approach until locating the interim software version most associated with the software operational error, and identifying the software version most associated with the software operational error as the defect root cause interim software version. Having identified the defect root cause interim software version, the DSN managing unit  18  outputs a test result that includes identification of the defect root cause interim software version. While the middle in  FIG. 11  is shown as the exact middle software version, other embodiments could use an approximate middle by rounding up or down as needed if a true middle does not exist. This approach will also work if something close to or near the middle is chosen, but the convergence rate will go down the further one gets from utilizing the middle. 
         [0052]      FIG. 10B  is a flowchart illustrating an example of identifying a software version associated with a software-based operational error. The method includes a step  100  where a processing module (e.g., of a distributed storage and task network (DSN) managing unit) detects a software-based operational error within a dispersed storage unit where a software test performed on a version of DS unit software produces an unfavorable result. The detecting includes one or more of interpreting an error message, selecting the software test from a plurality of software tests, facilitating exercising the DS unit software in accordance with the selected software test to produce a software test resultant, and indicating the software operational error when the software test resultant compares unfavorably to an expected software test resultant. 
         [0053]    The method continues at the step  102  where the processing module identifies a previous version of DS unit software associated with favorable DS unit operation, where the software test performed on the previous version of DS unit software produces a favorable result ( 1110  in  FIG. 11 ). For example, the processing module interprets a software test record using an identifier of the software test to identify the previous version of DS unit software associated with the favorable result. 
         [0054]    The method continues at the step  104  where the processing module identifies a sequential series of one or more interim DS unit software versions that ranges from the previous version of DS unit software to the current version of DS unit software ( 1112  in  FIG. 11 ). The identifying includes one or more of performing a lookup, interpreting a query result, interpreting an error message, and interpreting test records. 
         [0055]    The method continues at the step  106  where the processing module identifies a defect root cause interim DS unit software version associated with the software-based operational error utilizing a binary search software approach. The identifying includes, for a remaining sub-sequence of interim software versions, selecting a middle software version as a next test interim software version ( 1114  in  FIG. 11 ), selecting a software test associated with the software operational error and the next test interim software version, loading corresponding units (e.g. computing device  16  and/or DS units  36 ) and modules of the DSN with the next test interim software version, performing the selected software test on the DS unit software to produce a next test interim software version test result, indicating to move to an older interim software version (e.g., in the middle of a new remaining sub-sequence) when the test result is unfavorable or indicate to move to a newer interim software version when the test result is favorable, repeating the binary search approach until locating the interim software version most associated with the software operational error, and identifying the suffer version most associated with the software operational error as the defect root cause interim software version. The second and third interim versions, depending on the outcome of the first and second interim version tests, are illustrated in  FIG. 11  as  1116  and  1118  respectively. The subsequent interim versions to test can be determined in a similar matter. 
         [0056]    According to another embodiment, in certain high-priority times, when it is critical to isolate the test as rapidly as possible, or to complete an automated test as rapidly as possible, elastic-resources may be procured to instantiate multiple DS unit software instances on multiple DSN units and modules (e.g. computing device  16  and/or DS units  36 ) upon which different sub-sets of the full suite of automated tests can be independently executed. For example, if the test suite includes 200 tests, and 4 DS units are made available for a high-priority rushed test execution, then 50 of the 200 tests may be assigned to be executed against each of the 4 DS units. In this manner, each test failure can be rapidly traced to the underlying code change, and similarly, when fixed, this same system can rapidly determine the code change introduction that resulted in the fixed (as determined by a test that was failing but is now passing). 
         [0057]    The methods described above in conjunction with the computing device and the storage units can alternatively be performed by other modules of the dispersed storage network or by other devices. For example, any combination of a first module, a second module, a third module, a fourth module, etc. of the computing device and the storage units may perform the method described above. In addition, at least one memory section (e.g., a first memory section, a second memory section, a third memory section, a fourth memory section, a fifth memory section, a sixth memory section, etc. of a non-transitory computer readable storage medium) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices and/or by the storage units of the dispersed storage network (DSN), cause the one or more computing devices and/or the storage units to perform any or all of the method steps described above. 
         [0058]    As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item. 
         [0059]    As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal  1  has a greater magnitude than signal  2 , a favorable comparison may be achieved when the magnitude of signal  1  is greater than that of signal  2  or when the magnitude of signal  2  is less than that of signal  1 . As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship. 
         [0060]    As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture. 
         [0061]    One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. 
         [0062]    To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof. 
         [0063]    In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained. 
         [0064]    The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from Figure to Figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones. 
         [0065]    Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art. 
         [0066]    The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules. 
         [0067]    As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information. A computer readable memory/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. 
         [0068]    While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.