Patent Publication Number: US-2023161692-A1

Title: Shift left model execution in software delivery

Description:
BACKGROUND 
     The present disclosure relates generally to the field of application management and support (AMS), and more specifically to providing proactive guidance to users in order to execute a shift left model in software delivery. 
     Shift left is a practice intended to find and prevent defects early in the software delivery process. The idea is to improve quality by moving tasks to the left as early as possible in the lifecycle. Shift left testing means testing earlier in the software development process. 
     SUMMARY 
     Embodiments of the present disclosure include a method, computer program product, and system for providing proactive guidance to users in order to execute a shift left model in software delivery. A processor may receive an issue resolution request. The processor may access an issue resolution request repository. The issue resolution request repository may include details related to prior issue resolution requests. The processor may classify the issue resolution request based on the details related to the prior issue resolution requests. The processor may identify a root cause for the issue resolution request. 
     The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings included in the present disclosure are incorporated into, and form part of, the specification. They illustrate embodiments of the present disclosure and, along with the description, serve to explain the principles of the disclosure. The drawings are only illustrative of certain embodiments and do not limit the disclosure. 
         FIG.  1    illustrates a block diagram of an example proactive shift left guidance system, in accordance with aspects of the present disclosure. 
         FIG.  2 A  illustrates a flowchart of a high-level example method for providing proactive guidance to users in order to execute a shift left model in software delivery, in accordance with aspects of the present disclosure. 
         FIG.  2 B  illustrates a flowchart of a low-level example method for providing proactive guidance to users in order to execute a shift left model in software delivery, in accordance with aspects of the present disclosure. 
         FIG.  3 A  illustrates a cloud computing environment, in accordance with aspects of the present disclosure. 
         FIG.  3 B  illustrates abstraction model layers, in accordance with aspects of the present disclosure. 
         FIG.  4    illustrates a high-level block diagram of an example computer system that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein, in accordance with aspects of the present disclosure. 
     
    
    
     While the embodiments described herein are amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the particular embodiments described are not to be taken in a limiting sense. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure. 
     DETAILED DESCRIPTION 
     Aspects of the present disclosure relate generally to the field of application management and support (AMS), and more specifically to providing proactive guidance to users in order to execute a shift left model in software delivery. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context. 
     The proposed solution discussed throughout this disclosure is an AI-enabled solution which utilizes the benefit of shift left that can maximize any AMS and software development project. Shift left is a practice intended to find and prevent defects early in the software delivery process. The idea is to improve quality by moving tasks to the left as early as possible in the lifecycle. Shift left testing means testing earlier in the software development process. 
     Shift left should be a conscious effort to improve IT service delivery and support rather than something designed to save resources (e.g., capital, processing power, etc.) on a piecemeal basis. It is a strategy that is focused on a number of benefits, such as: speedier incident and service request resolution, cost reductions, better use of scarce technical know-how and capabilities, delivering a better end-user/customer experience, etc. 
     To achieve these benefits however, organizations/entities may need to appreciate that driving shift left change purely to save resources will most likely cause more of a detriment than help. For instance, an IT self-service capability that is designed and delivered purely to save resources will most likely not be used sufficiently to make a real difference to IT support operations or to deliver the expected resource benefits. 
     As already mentioned, shift left can make a significant difference to IT service management and IT support operations. In particular, in the unit cost (e.g., the cost per ticket/issue resolution request) of dealing with end-user/user/customer issues. For instance, it has been shown that the cost of Level 0 support is less than 10% of Level 1 costs. 
     For further reference, it has been shown that incident prevention costs $0 USD, Level 0 (self-help) costs around $2 USD, Level 1 (service desk help) costs around $22 USD, desktop support help costs around $69 USD, Level 2 (IT support help) costs around $104 USD, and Level 3 (vendor support help) costs around $599 USD. Thus, the concept of shift left means shifting issue help to the left, e.g., a lower level. 
     Accordingly, the more tickets/issue resolution requests that can be shifted to the left, the more inexpensive they become to resolve (or provision against). For example, a $22 USD Level 1 “human” password reset versus a $2 USD Level 0 automated password reset. 
     It is noted that in the traditional software development model, requirements are kept on the left side of the plan, and delivery and testing requirements are on the right side. The problem in such a development model is that these practices cannot handle changing expectations and requirements, which leads to negative outcomes such as increased costs, increased time to market, and unexpected errors. Another issue to arise is that with any large application support account, large amounts of tickets are received on a monthly basis and the application support IT team members/resources are generally a fixed count. Thus, shift left helps the application support organization to manage the support tickets in an optimal manner; some of the tickets can be solved by the business users while creating the ticket, and some tickets might need to be resolved by the Level 3 support team. Accordingly, the solution presented herein provides an appropriate guidance that will be provided to a business user for maximizing the benefit of shifting left. 
     Before turning to the FIGS. it may be beneficial to discuss the novelties of said solution: 
     While interacting with any business application by a (business) user, the proposed solution will predict any problem/issue the user might have to face associated with the application, and accordingly the proposed (AI) solution will proactively create/generate an appropriate guidance for the user so that the problem can be solved in a proactive manner by the user without creating/generating any service ticket/issue resolution request that will be forwarded to another level of help; 
     While creating/generating any service ticket by any user, the proposed solution will analyze the ticket based on the description from the user and/or application behavior and accordingly the proposed solution will identify an appropriate guidance for the user so that the ticket that is being created can be resolved immediately after the creation of the ticket; 
     If any ticket is created, with the AI nature of the solution, if the created ticket cannot be resolved by the user on their own, then, the proposed solution will analyze the ticket and predict the appropriate stage of service delivery/support level where the ticket can be resolved; 
     Based on historical data/information/details of creation of previous tickets by different users, the proposed solution will predict if appropriate user training is required so that the creation of the ticket can be reduced, and accordingly the proposed solution will create an appropriate training (e.g., a guidance sequence that is a training sequence) to interact with the applications so that the tickets can be reduced; 
     The proposed solution will identify if multiple users are creating same/similar types of tickets, or having same/similar problems/issues, and accordingly, the proposed solution will create an appropriate collaborative learning (guidance sequence) so that the identified users can solve the problem/issue together; 
     The proposed solution will identify a current level of engagement of the user(s), and determine/identify if providing a guidance is appropriate during that time in order to solve the problem/issue (e.g., determine if fixing the issue at a particular time is worth disengaging the user from their current task), and accordingly, the proposed solution will identify appropriate timing when the problem/issue can be solved with appropriate guidance; and/or 
     The proposed solution can be indexed with a type of device (e.g., a service module, etc.) and mode of access for solving the reported issues and also identify the resources needed for it, and accordingly, will be able to switch views, supplement the user with additional material, and/or initiate/invoke a collaborative channel to handshake with Level 3 support. 
     Referring now to  FIG.  1   , illustrated is block diagram of an example proactive shift left guidance system  100 , in accordance with aspects of the present disclosure. As depicted, the proactive shift left guidance system  100  includes a user  102 , a user device  104 , an interaction analysis module  106 , an issue resolution request  108 , an issue predictor  110  that includes an issue resolution request repository  112 , a knowledge corpus  114 , and a guidance (sequence)  116 . 
     In some embodiments, the user  102  is interacting with an application (not depicted) via the user device  104 . In some embodiments, the application may be partnered with the proactive shift left guidance system  100  (e.g., the application is subscribed to the proactive service of the system). The user  102  may begin to experience an issue/problem with the application and reach out for support for this issue via a support link within the application. The user then begins the creation/generation of the issue resolution request  108 . 
     In some embodiments, upon the creation/generation of the issue resolution request  108 , the user  102  is automatically asked to opt-in to allowing the interaction analysis module  106  to follow their interactions with the application and/or with their interaction/input associated with the issue resolution request  108 . In some embodiments, after opting-in, the user  102  may save their selection and if a subsequent issue arises, the interaction analysis module  106  may automatically begin following interactions. 
     In some embodiments, the interaction analysis module  106  begins to identify a specific/particular interaction or keyword the user  102  is using (e.g., the user  102  clicks multiple times on a link, the user  102  has included the words “not loading” in their issue resolution request, etc.). Upon identification of the specific/particular interaction and/or keyword, the interaction analysis module  106  begins to communicate with the issue predictor  110 . 
     The issue predictor  110  takes the information from the interaction analysis module  106  and accesses either or both the issue resolution request repository  112  and/or the knowledge corpus  114 . As depicted, the issue resolution request repository  112  and the knowledge corpus  114  may interact with one another to find a common  Nexus  between the interaction/keyword provided by the interaction analysis module  106  and other prior issue resolution requests (e.g., if the user  102  is clicking too fast, they may be causing a loading glitch, which was the case in a prior issue resolution request, etc.). In some embodiments, the prior issue resolution requests may be tagged with indicators or metadata (e.g., loading issue tag, multiple click tag, etc.) that help the issue predictor  110  locate the same or similar issues that the user  102  is experiencing. In some embodiments, the issue resolution request repository  112  may be incorporated with the knowledge corpus  114  (e.g., the knowledge corpus  114  may be housed within the issue resolution request repository  112 ). 
     In some embodiments, once the issue predictor  110  identifies a likely root cause for the issue with the application (e.g., as determined by a similarity between the issue resolution request  108 /interactions/keywords/etc.) the issue predictor  110  generates the guidance  116  that may be provided to the user  102  via the user device  104 . In some embodiments, the guidance  116  may be any of: a guided sequence for the user to try to resolve the issue (e.g., use command “r” to reload the page, then click the link once), a guided list of words to present in the issue resolution request  108  that will help a different level support operator (e.g., indicate that the page has been frozen for 20 mins), an automatic issue resolution request to preload as the issue resolution request  108  (e.g., this requests mirrors exactly issue resolution request #XXYY from the issue resolution request repository  112 ), etc. 
     In some embodiments, as depicted, if the user  102  does not opt-in to the interaction analysis module  106  following their interactions, the user  102  can finish the issue resolution request  108  and it can be used by the issue predictor  110  to predict the issue and provide the guidance  116 . 
     Turning to a more in-depth example associated with the proactive shift left guidance system  100 , the proactive shift left guidance system  100  will have an AI-module (e.g., processor) to classify tickets (e.g., issue resolution requests) created/generated by different users. 
     The proactive shift left guidance system  100  will historically track ticket details from a ticket repository (e.g., issue resolution request repository  112 ), and the ticket detail(s) will be a: ticket description, ticket resolution steps, who created the ticket, when the ticket was/is created, application name associated with the ticket/issue, resolver group (e.g., which level of support), etc. 
     In some embodiments, based on historically gathered ticket details, the proactive shift left guidance system  100  will classify the tickets, where the classification of the tickets can be based on resolver group, types of solutions applied, etc. Further, based on the ticket details, the proactive shift left guidance system  100  will identify a possible root cause of the tickets; generally root causes are provided while any ticket is resolved. 
     In some embodiments, based on historical information of different tickets the proactive shift left guidance system  100  will create/generate the knowledge corpus  114  about the tickets. In some embodiments, while interacting with any application by a user, the proactive shift left guidance system  100  will also be analyzing/following/tracking user interaction(s) with the application (using the interaction analysis module  106 ), and will track when the user is creating any ticket. 
     Based on historical interactions with the application, and a ticket creation at a later point of time, the proactive shift left guidance system  100  will predict (using the issue predictor  110 ) if any activity is problematic, and predict a reason for creating the ticket. 
     The proactive shift left guidance system  100  will create the knowledge corpus  114  about the user  102 &#39;s interaction behavior with the application which can be an indication that the user  102  is having a problem with the application which has led to the creation of the ticket (e.g., issue resolution request  108 ). In some embodiments, based on the created knowledge corpus  114 , the proactive shift left guidance system  100  will predict what types of problems the user  102  is having and what types of ticket can be created by the user  102 . 
     In some embodiments, based on the knowledge corpus  114 , the proactive shift left guidance system  100  will be able to identify types of resolutions (e.g., guidance  116 ) and resolution steps (e.g., guidance sequences) for any classified ticket. 
     In some embodiments, while the user  102  is interacting with any application, then the proactive shift left guidance system  100  will identifying/analyze user  102  interaction behavior with the application, and generate an application log associated with the interaction behavior. The proactive shift left guidance system  100  will predict, from the interaction behavior and/or application log, what types of problem the user  102  may be having and what types of tickets can be created based on the interaction behavior and/or application log. 
     In some embodiments, the identified information from the interaction behavior and/or application log, will be used to predict a possible root cause of the problem, such as if the problem can be resolved with training to the user  102 . In such an embodiment, the proactive shift left guidance system  100  will identify appropriate training and provide the training (e.g., guidance  116 ) to the user  102  to solve the problem. 
     In some embodiments, if the proactive shift left guidance system  100  predicts any problem(s), then the proactive shift left guidance system  100  will show/provide appropriate guidance (e.g.,  116 ) to the user  102  so that, without creating a ticket, the user  102  can (re)solve the problem. 
     In some embodiments, the resolution can be provided with an AR/VR device, a text overlay, and/or audio information to the user  102  so that the problem can be resolved without creating a ticket. Based on the guidance  116  to the user  102 , the proactive shift left guidance system  100  can understand how the problem can be solved, and the user  102  does not have to create the ticket. 
     In some embodiments, while creating any ticket, the proactive shift left guidance system  100  will read textual information in the tickets, and according guide the user  102  to show how the problem can be solved. In some embodiments, the proactive shift left guidance system  100  will evaluate if the user  102  can be guided to solve the problem without creating the ticket and if an appropriate guidance (e.g.,  116 ) is not available then the proactive shift left guidance system  100  will automatically and directly assign the ticket to an appropriate group (e.g., support level). 
     Referring now to  FIG.  2 A , illustrated is a flowchart of a high-level example method  200  for providing proactive guidance to users in order to execute a shift left model in software delivery, in accordance with aspects of the present disclosure. In some embodiments, the method  200  may be performed by a processor (e.g., of the proactive shift left guidance system  100 , etc.). 
     In some embodiments, the method  200  begins at operation  202 , where the processor receives an issue resolution request (e.g., a ticket). In some embodiments, the method  200  proceeds to operation  204 , where the processor accesses an issue resolution request repository. The issue resolution request repository may include details (e.g., tags associated with issues, classification type of the issues, etc.) related to/associated with prior issue resolution requests (e.g., where the prior issue resolution requests could be related to an assortment of other respective users). 
     In some embodiments, the method  200  proceeds to operation  206 , where the processor classifies the issue resolution request based on the details related to the prior issue resolution requests. In some embodiments, the method  200  proceeds to operation  208 , where the processor identifies a root cause for the issue resolution request (e.g., the installation of the application was corrupted, the user is using the wrong tool, etc.). 
     In some embodiments, discussed below, there are one or more operations of the method  200  not depicted for the sake of brevity and which are discussed throughout this disclosure. Accordingly, in some embodiments, the processor may further incorporate the issue resolution request into the issue resolution requests repository and generate a knowledge corpus in the issue resolution request repository. In some embodiments, the knowledge corpus may include predicted connections (e.g., a common  Nexus ) between the issue resolution request and the prior issue resolution requests (e.g., if classified as X then likely root cause is Y and this X-classified issue could then relate to this other ticket that is having this same X-classified issue, or it is known that issues related to Y-root cause are known to have a Z-classified issue too, etc.). 
     In some embodiments, the processor may further identify a generation of the issue resolution request and analyze/follow user interactions with an application. The application may be associated with the issue resolution request. The processor may further predict that an issue with the application will occur, based on the classifying of the issue resolution request and the analyzing of user interactions. The processor may generate an indication about the issue and provide the indication to a user (or users depending on the application). 
     In some embodiments, the indication is a guidance, and the processor further predicts a classification for the issue resolution request. The classification may be associated with a specific issue (e.g.,  404  error, lag issues, etc.). The processor may identify a resolution for the specific issue and provide the guidance to the suer to pre-empt the specific issue (e.g., provide training to the user on how to use the application/fix the issue, how to fill in the issue resolution request such that it is filtered to a correct level of support, etc.). 
     In some embodiments, the processor may further identify that the guidance pre-empted the specific issue and provide the user an opportunity to cancel the generation of the issue resolution request. In some embodiments, if the processor identifies that the guidance pre-empted the specific issue (e.g., prevented the specific issue from occurring or resolved the specific issue) the processor may automatically stop the generation of the issue resolution request. 
     In some embodiments, the processor may identify that the guidance did not pre-empt the specific issue and provide the user with an addendum (e.g., an add-on, an update, etc.) to the guidance. The addendum may provide concise terms to include in the issue resolution request (which may help filter the issue resolution request to a correct level of support). In some embodiments, the addendum may provide an attrition step or avenue for the user to attempt in order to resolve the specific issue. 
     Turning now to  FIG.  2 B , illustrated is a flowchart of a low-level example method  220  for providing proactive guidance to users in order to execute a shift left model in software delivery, in accordance with aspects of the present disclosure. As depicted, in some embodiments, the method  220  may have operations  222  and  224  performed by a processor. The processor, at operation  222 , may analyze user interaction(s) with an application and at operation  224 , the processor may record the user interaction(s) to an application log. The analysis and recordings at respectively at operations  222  and  224  may then be used by the processor to identify, at operation  226 , an issue while a user interacts with the application. 
     In some embodiments, after operation  226 , the processor, at operation  228 , may associate the issue with a classification of issue resolution requests. In some embodiments, after operation  228 , the processor, at operation  236 , may identify a root cause associated with the issue (of the issue resolution request). In some embodiments, before the method  220  proceeds to operation  236 , the processor may identify, at operation  230 , that generation of an issue resolution request has been initiated; this identification could be used in communication with operation  228  to find a correct classification association. 
     In some embodiments, the processor may resolve, at operation  232 , the issue resolution request and provide details (e.g., classifications, metadata, etc.) to a knowledge corpus that could help resolve subsequent issue resolution requests. In some embodiments, the processor, at operation  234 , may classify the issue resolution request. In some embodiments, after operation  234  and/or simultaneously with operation  228 , the processor may identify, at operation  236 , the root cause. 
     In some embodiments, after operation  236 , the processor may identify, at operation  238 , a guidance. In some embodiments, in identifying a guidance at operation  238 , the processor may predict, at operation  240 , the issue associated with the issue resolution request. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present disclosure are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as Follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of portion independence in that the consumer generally has no control or knowledge over the exact portion of the provided resources but may be able to specify portion at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as Follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as Follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
       FIG.  3 A , illustrated is a cloud computing environment  310  is depicted. As shown, cloud computing environment  310  includes one or more cloud computing nodes  300  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  300 A, desktop computer  300 B, laptop computer  300 C, and/or automobile computer system  300 N may communicate. Nodes  300  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. 
     This allows cloud computing environment  310  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  300 A-N shown in  FIG.  3 A  are intended to be illustrative only and that computing nodes  300  and cloud computing environment  310  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
       FIG.  3 B , illustrated is a set of functional abstraction layers provided by cloud computing environment  310  ( FIG.  3 A ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  3 B  are intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted below, the following layers and corresponding functions are provided. 
     Hardware and software layer  315  includes hardware and software components. Examples of hardware components include: mainframes  302 ; RISC (Reduced Instruction Set Computer) architecture based servers  304 ; servers  306 ; blade servers  308 ; storage devices  311 ; and networks and networking components  312 . In some embodiments, software components include network application server software  314  and database software  316 . 
     Virtualization layer  320  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  322 ; virtual storage  324 ; virtual networks  326 , including virtual private networks; virtual applications and operating systems  328 ; and virtual clients  330 . 
     In one example, management layer  340  may provide the functions described below. Resource provisioning  342  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  344  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  346  provides access to the cloud computing environment for consumers and system administrators. Service level management  348  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  350  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  360  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  362 ; software development and lifecycle management  364 ; virtual classroom education delivery  366 ; data analytics processing  368 ; transaction processing  370 ; and providing proactive guidance to users in order to execute a shift left model in software delivery  372 . 
       FIG.  4   , illustrated is a high-level block diagram of an example computer system  401  that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system  401  may comprise one or more CPUs  402 , a memory subsystem  404 , a terminal interface  412 , a storage interface  416 , an I/O (Input/Output) device interface  414 , and a network interface  418 , all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus  403 , an I/O bus  408 , and an I/O bus interface unit  410 . 
     The computer system  401  may contain one or more general-purpose programmable central processing units (CPUs)  402 A,  402 B,  402 C, and  402 D, herein generically referred to as the CPU  402 . In some embodiments, the computer system  401  may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system  401  may alternatively be a single CPU system. Each CPU  402  may execute instructions stored in the memory subsystem  404  and may include one or more levels of on-board cache. 
     System memory  404  may include computer system readable media in the form of volatile memory, such as random access memory (RAM)  422  or cache memory  424 . Computer system  401  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  426  can be provided for reading from and writing to a non-removable, non-volatile magnetic media, such as a “hard drive.” Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), or an optical disk drive for reading from or writing to a removable, non-volatile optical disc such as a CD-ROM, DVD-ROM or other optical media can be provided. In addition, memory  404  can include flash memory, e.g., a flash memory stick drive or a flash drive. Memory devices can be connected to memory bus  403  by one or more data media interfaces. The memory  404  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments. 
     One or more programs/utilities  428 , each having at least one set of program modules  430  may be stored in memory  404 . The programs/utilities  428  may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Programs  428  and/or program modules  430  generally perform the functions or methodologies of various embodiments. 
     Although the memory bus  403  is shown in  FIG.  4    as a single bus structure providing a direct communication path among the CPUs  402 , the memory subsystem  404 , and the I/O bus interface  410 , the memory bus  403  may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface  410  and the I/O bus  408  are shown as single respective units, the computer system  401  may, in some embodiments, contain multiple I/O bus interface units  410 , multiple I/O buses  408 , or both. Further, while multiple I/O interface units are shown, which separate the I/O bus  408  from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses. 
     In some embodiments, the computer system  401  may be a multi-user mainframe computer system, a single-user system, or a server computer or similar device that has little or no direct user interface, but receives requests from other computer systems (clients). Further, in some embodiments, the computer system  401  may be implemented as a desktop computer, portable computer, laptop or notebook computer, tablet computer, pocket computer, telephone, smartphone, network switches or routers, or any other appropriate type of electronic device. 
     It is noted that  FIG.  4    is intended to depict the representative major components of an exemplary computer system  401 . In some embodiments, however, individual components may have greater or lesser complexity than as represented in  FIG.  4   , components other than or in addition to those shown in  FIG.  4    may be present, and the number, type, and configuration of such components may vary. 
     As discussed in more detail herein, it is contemplated that some or all of the operations of some of the embodiments of methods described herein may be performed in alternative orders or may not be performed at all; furthermore, multiple operations may occur at the same time or as an internal part of a larger process. 
     The present disclosure may be a system, a method, and/or a computer program product at any possible technical detail level of integration. 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 disclosure. 
     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 disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, 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 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 disclosure. 
     Aspects of the present disclosure 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 disclosure. 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 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 disclosure. 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 blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, 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. 
     The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     Although the present disclosure has been described in terms of specific embodiments, it is anticipated that alterations and modification thereof will become apparent to the skilled in the art. Therefore, it is intended that the following claims be interpreted as covering all such alterations and modifications as fall within the true spirit and scope of the disclosure.