FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
SYSTEM AND METHOD FOR DETERMINING RELEASE STABILITY OF APPLICATIONS BASED ON A RELEASE STABILITY INDEX
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD [001] The disclosure herein generally relates to applications management, and, more particularly, to system and method for determining release stability of applications based on a release stability index.
BACKGROUND [002] At present scenario, a greater visibility and predictability across life cycle of one or more applications to ensure that impediments across release phases are handled seamlessly to maintain overall velocity. This would require an end to end traceability and process compliance across the quality engineering building blocks, resulting in enhancing visibility and predictability in the quality engineering process and also improves the time to market with greater confidence. The discovery of the data from one or more desperate sources was the biggest challenge. Recent adoption for frequent releases opens up new set of challenges and associated risk, hence there is need of real-time assessments of data to predict the release confidence for refining the quality process so that one or more expectations associated with business are continuously met. Further, existing approaches for calculating the quality of the release of the applications are moreover manual, rule based and not so comprehensive that does not produce right results to instill the confidence in stakeholders.
SUMMARY [003] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method of determining stability of a release of applications based on a release stability index is provided. The processor implemented method includes at least one of: configuring, via one or more hardware processors, a plurality of data sources to import a relevant data; extracting, via the one or more hardware processors, a plurality of data from the configured plurality of data sources; deriving, via the one or more hardware processors, a

plurality of rules and a plurality of policies based on the plurality of data; deriving, via the one or more hardware processors, a release stability model for at least one of plurality of phases of a release management cycle; learning, via the one or more hardware processors, the derived release stability model to analyze at least one data pattern associated with at least one parameter of the plurality of phases to compute a metrics; determining, via the one or more hardware processors, the release stability index based on the derived at least one data pattern to predict the stability of the release of the plurality of applications. In an embodiment, the plurality of data corresponds to at least one of: (i) a structured data, (ii) an un-structured data, and combination thereof.
[004] In an embodiment, the relevant data may correspond to a historical and a real-time data to create a knowledge base and a plurality of data models for training. In an embodiment, the plurality of phases of the release management cycle may corresponds to at least one of: (i) plan, (ii) develop, (iii) build, (iv) test, and (v) deploy. In an embodiment, the release stability model may be derived based on at least one of: (a) one or more data patterns observed over time between how the stability of release is impacting based on an identified impacting attributes, (b) a derived release stability index value, and (c) rules and respective weightage are applied to different impacting attributes to identify importance or to prioritize a learning based on one or more high impacting attributes. In an embodiment, the release stability index may be calculated using a weighted sum of parameters to determine how much stability achieved by the release of the plurality of applications. In an embodiment, a higher value of the release stability index may indicate a high stability and a lower value of the release stability index indicate a low stability.
[005] In another aspect, there is provided a system to determine stability of a release of applications based on a release stability index. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces. The one or more hardware processors are configured by the instructions to: configure, a plurality of data sources to import a relevant data;

extract, a plurality of data from the configured plurality of data sources; derive, a plurality of rules and a plurality of policies based on the plurality of data; derive, a release stability model for at least one of plurality of phases of a release management cycle; learn, the derived release stability model to analyze at least one data pattern associated with at least one parameter of the plurality of phases to compute a metrics; determine, the release stability index based on the derived at least one data pattern to predict the stability of the release of the plurality of applications. In an embodiment, the plurality of data corresponds to at least one of: (i) a structured data, (ii) an un-structured data, and combination thereof.
[006] In an embodiment, the relevant data may correspond to a historical and a real-time data to create a knowledge base and a plurality of data models for training. In an embodiment, the plurality of phases of the release management cycle may corresponds to at least one of: (i) plan, (ii) develop, (iii) build, (iv) test, and (v) deploy. In an embodiment, the release stability model may be derived based on at least one of: (a) one or more data patterns observed over time between how the stability of release is impacting based on an identified impacting attributes, (b) a derived release stability index value, and (c) rules and respective weightage are applied to different impacting attributes to identify importance or to prioritize a learning based on one or more high impacting attributes. In an embodiment, the release stability index may be calculated using a weighted sum of parameters to determine how much stability achieved by the release of the plurality of applications. In an embodiment, a higher value of the release stability index may indicate a high stability and a lower value of the release stability index indicate a low stability.
[007] In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: configuring, a plurality of data sources to import a relevant data; extracting, a plurality of data from the configured plurality of data sources; deriving, a plurality of rules and a plurality of policies based on the plurality of data; deriving, a release stability model for at least one of plurality of phases of a release

management cycle; learning, the derived release stability model to analyze at least one data pattern associated with at least one parameter of the plurality of phases to compute a metrics; determining, the release stability index based on the derived at least one data pattern to predict the stability of the release of the plurality of applications. In an embodiment, the plurality of data corresponds to at least one of: (i) a structured data, (ii) an un-structured data, and combination thereof.
[008] In an embodiment, the relevant data may correspond to a historical and a real-time data to create a knowledge base and a plurality of data models for training. In an embodiment, the plurality of phases of the release management cycle may corresponds to at least one of: (i) plan, (ii) develop, (iii) build, (iv) test, and (v) deploy. In an embodiment, the release stability model may be derived based on at least one of: (a) one or more data patterns observed over time between how the stability of release is impacting based on an identified impacting attributes, (b) a derived release stability index value, and (c) rules and respective weightage are applied to different impacting attributes to identify importance or to prioritize a learning based on one or more high impacting attributes. In an embodiment, the release stability index may be calculated using a weighted sum of parameters to determine how much stability achieved by the release of the plurality of applications. In an embodiment, a higher value of the release stability index may indicate a high stability and a lower value of the release stability index indicate a low stability.
[009] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[010] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[011] FIG. 1 illustrates an exemplary system to determine a release stability of a plurality of applications based on a release stability index, according to some embodiments of the present disclosure.

[012] FIG. 2 is a functional block diagram of the exemplary release stability determination system to determine the release stability of the plurality of applications based on the release stability index, according to some embodiments of the present disclosure.
[013] FIG. 3 is an exemplary flow diagram illustrating a method of data analyzing and decision-making by a data analytics and decision-making module, according to some embodiments of the present disclosure.
[014] FIG. 4 is an exemplary functional block diagram illustrating the release stability index at a plurality of phases of a release development cycle, according to some embodiments of the present disclosure.
[015] FIG. 5A-5E is an exemplary functional block diagram illustrating a plurality of parameters considered at the plurality of phases of the release development cycle, according to some embodiments of the present disclosure.
[016] FIG. 6 is an exemplary flow diagram illustrating a method for determining the release stability of the plurality of applications based on the release stability index, according to some embodiments of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS [017] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims.
[018] Embodiments herein provides a platform that validates compliance of current and planned releases against industry standards and best practices to predict a stability of a release to take a decision for a release of a plurality of applications based on a computed release stability index and further to measure

health of the release of the plurality of applications. The platform smoothly integrates their existing Applications life cycle management (ALM) toolsets for one or more stages of the application life cycle such as plan, build, code and test. The platform harnesses the application life cycle data to generate contextual insights, which enable teams to collaborate for achieving high-velocity quality releases. The embodiment of the present disclosure utilizes an adaptive learning-based system to apply one or more supervised algorithms to classify data, recognizing patterns and weightages to optimize one or more predictions and recommendations.
[019] Referring now to the drawings, and more particularly to FIGS. 1 through 6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[020] FIG. 1 illustrates an exemplary system 100 to determine a release stability of a plurality of applications based on a release stability index, according to some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The memory 102 comprises a database 108. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
[021] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of

networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
[022] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 102 includes a plurality of modules and a repository for storing data processed, received, and generated by the plurality of modules. The plurality of modules may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.
[023] Further, the database 108 stores information pertaining to inputs fed to the system 100 and/or outputs generated by the system 100 (e.g., data/output generated at each stage of the data processing), specific to the methodology described herein. More specifically, the database 108 stores information being processed at each step of the proposed methodology. In an embodiment, the database 108 include a knowledge database. The repository, amongst other things, includes a system database and other data. The other data may include data generated as a result of the execution of one or more modules in the plurality of modules.
[024] FIG. 2 is a functional block diagram of diagram of exemplary release stability determination system 200 to determine the release stability of the plurality of applications based on the release stability index, according to some embodiments of the present disclosure. The system 100 include a data discovery module 202, a policy and rule module 204, and the data analytics and decision-making module 206. The data discovery module 202 is configured to discover a historical and real¬time data as well to create a knowledge base and a plurality of data models for training. The data discovery module 202 is configured to extracts a structured and

an un-structured data from multiple sources but not limited to such as real-time data from tools within platform, a transactional data from pipeline and platform services etc. In an embodiment, a plurality of phases is alternatively referred as a plurality of stages.
[025] In an embodiment, the data is collected, loaded and refreshed in the database 108 using at least one of: (i) data imported from integrated Tools accessed through database (DB) or Representational state transfer (REST) APIs, (ii) Bulk upload of the data using comma separated value (CSV), .XLS, logs of execution, and (iii) manually entered data from user interface (UI) Screens. The data discovery module 202 include at least one of: (i) adaptor for data discovery (JIRA/HP-ALM/Octane/Rally/Service-Now and other tools in Smart quality engineering (QE), (ii) capability for SQE entities mapping with discovered entities from tool, (iii) capability to capture data points of user interactions, and (iv) capabilities to continuously collecting and refreshing information on the objects that is previously discovered.
[026] The policy and rule module 204 is capable of allowing one or more users to create their own rules and policies for a release compliance perspective and to provide inputs to analytics algorithm to calculate the release stability index. In an embodiment, one or more policy is a guideline or set of standard that applies on entity, group of entities to check compliance as per defined standard. In an embodiment, the one or more policies can be used as checkpoints or gates to control a life cycle of the plurality of applications. In an embodiment, the one or more policies include a scope, including entities, releases, one or more release phases, and one or more rules, which validate the objects in scope. In an embodiment, each policy can contain one or more rules and the rule is used to validate objects in scope for compliance. A rule includes one or more or conditions, referred the desired state, to validate the compliance. A violation of a rule is referred as a breach, or a policy breach. In an embodiment, the breach or one or more issue triggers when data points in scope that does not comply with the desired state of rule. In an embodiment, each rule can be assigned to weightage based on importance and contribute in index, below are some samples rule.

[027] In an exemplary embodiment, the below table. 1 refers one or more rules considered in the release stability index:

S.No. Rules
1 Code Commits frequency
2 Code Quality Index (Technical Debt)
3 Automated Builds per day
4 CI Build Success/Failure Rate
5 Unit Test Successes Rate
6 Code Coverage
7 Test Execution coverage (%)
8 Percentage successful/Failed deployments
9 No. of Open defects with Severity
10 No. of defects need to be verified
11 The number of branching paths within code in all the source code
files in a release.
12 The number of days since the previous release.
13 Number of new requirements added
14 Number of defects to be fixed
15 Lines of code added
Table. 1
[028] In an embodiment, the policy and rule module 204 of the QE map analyzes one or more risks based on various parameters like code coverage, technical debt, build failure, regression coverage, number of open defects, number of lines added in code etc.
[029] The data analytics and decision-making module 206 is configured to analyze the mined data to derive a data model to support prediction of the release stability and compliance for the release. In an embodiment, the platform utilizes a

historical data associated with release of the plurality of applications, compliance status and associated stability to create the data model.
[030] The data analytics and decision-making module 206 utilizes regression which is a parametric technique to predict continuous (dependent) variable given a set of independent variables. In an embodiment, the regression is parametric in nature as it performs certain assumptions based on one or more data sets. The regression uses a linear function to approximate a dependent variable given as:
Y = βo + β1x + ∈
Where, Y - Dependent variable, x - Independent variable, βo - Intercept, β1 - Slope, ∈ - Error.
[031] In an embodiment, a classification, also referred as categorization, is a machine learning technique that uses a known data to determine how a new data should be classified into a set of existing categories. In an embodiment, the classification is a form of a supervised learning.
[032] In an embodiment, the data analytics and decision-making module 206 utilizes two-regression algorithm i.e. based on number of input and output variables, and a binary classifier. In an embodiment, a multivariate regression pertains to a plurality of dependent variables and a plurality of independent variables:
y1,y2 ym=f(x1,x2 xn)
[033] In an embodiment, a multiple linear regression pertains to one dependent variable and a plurality of independent variables:
y = f(x1,x2 xn)
[034] In an embodiment, a Naïve Bayes classifier is a probabilistic machine-learning model that is used for classification task. The crux of the classifier is based on the Bayes theorem.
P(y|X) = {P(X|Y) P(y)}/P(X)
Where, y is a class variable and X is a dependent feature vector.

[035] The platform of the present disclosure is based on an ensemble model include the multivariate linear regression, the multiple linear regression and the binary classifier to predict the release stability health and the release stability index at various stages or phases of the plurality of applications (e.g., one or more software applications) development release cycle. In an embodiment, the release cycle of a software includes the plurality of stages: (i) Plan, (ii) Develop, (iii) Build, (iv) Test, and (v) Deploy. At each stage of software development life cycle, projected release stability index is computed and classifying the current health of the release as Stable or Not Stable. At the deployment, stage of the release cycle, a final release stability index can be computed and assigned to the release of the plurality of applications.
[036] In an embodiment, the platform utilizes an appropriate regression algorithm by considering different available parameters for one or more different stages to predict the parameters for the next stages. In an embodiment, the multiple linear regression algorithm is applied where the one or more stages with single output variable. Further, the multivariate linear regression algorithm is applied at the stages where the prediction of multiple dependent variables. The binary classifier utilizes the computed values at each stage to predict the stage “build” as “Stable” or “Not Stable”.
[037] In an embodiment, the release stability index is an index to be calculated using the weighted sum of the one or more parameters to determine how much stability is achieved by the release of the plurality of the applications. In an embodiment, a higher value implies more stability and a lower value symbolizes a low stable release. The release stability index (RSI) can be calculated as follows:

Where, αi= weightage assigned to a identified rule in the policy (e.g., value lies between 1 to 5); βi =Rule status (0/1); and n = number of rules added in the policy. For example, assigning the weightage or importance as a number to the rules while defining the policy. Thus, the value of RSI lies between 0 to 100 and symbolizes the release heath.

[038] In an embodiment, a rule status is said to be 1 if qualifies the threshold value defined in the policy, otherwise is considered as 0.
[039] In an embodiment, the prediction model is derived based on: (a) one or more data patterns observed over time between how the stability of release is impacting based on the identified impacting attributes, and (b) Rules and respective weightage are applied to different impacting attributes to identify importance or to prioritize a learning based on one or more high impacting attributes.
[040] FIG. 3 is an exemplary flow diagram illustrating a method of data analyzing and decision-making by the data analytics and decision-making module 206, according to some embodiments of the present disclosure. The data analytics and decision-making module 206 is configured to analyze at each stage the number of independent variables and compute the dependent variable. In an embodiment, number of independent variables leads to selection of the appropriate regression algorithm. At first step, the number of dependent variables (y) is determined. If the dependent variable is one in number, then selecting the multiple linear regression algorithm, whereas if it is more in number then the multivariate linear regression algorithm is utilized. The data analytics and decision-making module 206 acts as a black box of this ensemble model where the input is passed through and appropriate processing take place to generate the desired output.
[041] FIG. 4 is an exemplary functional block diagram illustrating the release stability index at the plurality of phases of a release development cycle, according to some embodiments of the present disclosure. In an embodiment, the release cycle of a software includes the plurality of stages: (i) Plan, (ii) Develop, (iii) Build, (iv) Test, and (v) Deploy. For example, Table. 2 refers to one or more parameters considered for the analysis at each stage and assumed values for calculation:

Release Cycle Stage Parameters Considered (Identified Rules) Assigned Weightage (αi) Sample
considered
value Threshold Considered
Plan Number of new requirement 3 6 5

Number of
defects to be
fixed 1 15 10
Develop Lines of code added 2 600 1000

Unit Test Success Rate 2 90 75
Build Build Failure Rate 5 20 15
Test Test Execution Coverage 2 95 90
Deploy Deployment Failure Rate 2 20 15
Table. 2
[042] The parameters are the considered rules in the policy, and each are assigned with a weightage as a part of creating the policy.
[043] FIG. 5A-5E is an exemplary functional block diagram illustrating the plurality of parameters considered at the plurality of phases of the release development cycle, according to some embodiments of the present disclosure. For example, Table. 3 refers to one or more calculated “rule status” from values in Table 2:

Parameters Considered (Identified Rules) Threshold Considered Rule Status(βi )
Number of new requirement 5 1
Number of defects to be fixed 10 1
Lines of code added 1000 0
Unit Test Success Rate 75 1
Build Failure Rate 15 1
Test Execution Coverage 90 1
Deployment Failure Rate 15 1
Table. 3 [044] Stage 1: Predicting Release Stability at Plan stage:

Input Parameters:
i. Number of new requirement ii. Number of defects to be fixed
Output Parameter:
i. Projected Lines of code needs to be added.

Output Parameters Threshold Considered Projected Values
at this stage by the
algorithm
Lines of code added 1000 600
Unit Test Success Rate 75 90
Build Failure Rate 15 20
Test Execution Coverage 90 95
Deployment Failure Rate 15 20
[045] In an embodiment, at the plan stage, two input parameters are considered for predicting the projected lines of code that needs to be added based on the historical data of the release. Further, this can be utilized for predicted other parameters of the later stages using the multivariate linear regression algorithm. For example, a Projected Unit Test Success, a Projected Build Failure Rate, a Projected Test Execution Coverage, a Projected Deployment Failure Rate are provided using the input and output parameters of the plan phase. Further, one or more projected values are finally contributing in calculating the release as Stable or Not Stable using the Naïve Bayes classifier. The projected values can further be utilized to present Release Stability Index. The weightage is defined as user defined variable while creating the policy. The RSI can be calculated using equation 1 as follows:

[046] Stage 2: Predicting Release Stability at Develop stage:

Input Parameters:
▪ Number of new requirement ▪ Number of defects to be fixed ▪ Lines of code added ▪ Unit Test Success Rate
Output Parameter:
▪ Projected Build Failure Rate ▪ Projected Test Execution Coverage ▪ Projected Deployment Failure Rate [047] In an embodiment, at the plan stage, four input parameters are considered for predicting the projections of Test Execution Coverage, build success/failure rate, deployment success/failure rate that needs to be added based on the historical data of the release using the multivariate regression algorithm as to extract multiple dependent variables. Further, one or more projected values are finally contributing in calculating the release as Stable or Not Stable using Naïve Bayes classifier. The projected values can further be utilized to present the Release Stability Index as performed in stage 1.
[048] Stage 3: Predicting Release Stability at Build stage: Input Parameters:
• Number of new requirement
• Number of defects to be fixed
• Lines of code added
• Unit Test Success Rate
• Build Failure Rate
Output Parameter:
• Projected Test Execution Coverage
• Projected Deployment Failure Rate
[049] In an embodiment, at the plan stage, five input parameters are considered for predicting the projections of Test Execution Coverage, deployment success/failure rate that needs to be added based on the historical data of the release

using the multivariate regression algorithm as to extract multiple dependent variables. The projected values are finally contributing in calculating the release as Stable or Not Stable using the Naïve Bayes classifier. The projected values can further be utilized to present the Release Stability Index as performed in stage 1. [050] Stage 4: Predicting Release Stability at Test stage: Input Parameters:
• Number of new requirement
• Number of defects to be fixed
• Lines of code added
• Unit Test Success Rate
• Build Failure Rate
• Test Execution Coverage
Output Parameter:
• Projected Deployment Failure Rate
[051] In an embodiment, at the plan stage, six input parameters are considered for predicting the projections of deployment success/failure rate that needs to be added based on the historical data of the release using the best subset linear regression algorithm as we have single multiple dependent variable. The projected values are finally contributing in calculating the release as Stable or Not Stable using Naïve Bayes classifier. The projected values can further be utilized to present the Release Stability Index as performed in stage 1.
[052] Stage 5: Predicting Release Stability at Deploy: Input Parameters:
• Number of new requirement
• Number of defects to be fixed
• Lines of code added
• Unit Test Success Rate
• Build Failure Rate
• Test Execution Coverage
• Deployment Failure Rate
Output Parameter:
• Release Stability

[053] In an embodiment, all the stage includes actual values that are finally contributing in classifying the release as Stable or Not Stable using Naïve Bayes classifier. The final Release Stability Index can be presented for the release at this stage using the mentioned formula in equation 1 and as described in detail at stage 1.
[054] FIG. 6 is an exemplary flow diagram illustrating a method for determining the release stability of the plurality of applications based on the release stability index, according to some embodiments of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The flow diagram depicted is better understood by way of following explanation/description. The steps of the method of the present disclosure will now be explained with reference to the components of the system as depicted in FIGS. 1 and 2.
[055] At step 602, a plurality of data sources is configured to import a relevant data. In an embodiment, the relevant data may correspond to a historical and a real-time data to create a knowledge base and a plurality of data models for training. At step 604, a plurality of data is extracted from the configured plurality of data sources. In an embodiment, the plurality of data corresponds to at least one of: (i) a structured data, (ii) an un-structured data, and combination thereof. At step 606, a plurality of rules and a plurality of policies is derived based on the plurality of data. At step 608, a release stability model is derived for at least one of the plurality of phases of a release management cycle. In an embodiment, the plurality of phases of the release management cycle may corresponds to at least one of: (i) plan, (ii) develop, (iii) build, (iv) test, and (v) deploy. In an embodiment, the release stability model may be derived based on at least one of: (a) one or more data patterns observed over time between how the stability of release is impacting based on an identified impacting attributes, (b) a derived release stability index value, and (c) rules and respective weightage are applied to different impacting attributes to

identify importance or to prioritize a learning based on one or more high impacting attributes.
[056] In an embodiment, the release stability index may be calculated using a weighted sum of parameters to determine how much stability achieved by the release of the plurality of applications. In an embodiment, a higher value of the release stability index may indicate a high stability and a lower value of the release stability index indicate a low stability. At step 610, the derived release stability model is learned to analyze at least one data pattern associated with at least one parameter of the plurality of phases to compute a metrics. At step 612, the release stability index is determined based on the derived at least one data pattern to predict the stability of the release of the plurality of applications.
[057] The embodiments of the present disclosure provide a systematic and an automated solution for computing a release stability index based on an artificial intelligence (AI) and a machine learning (ML) techniques for predictive decision-making for release decision, measuring and improving the application release quality. The embodiments of the present disclosure provide a solution to establish co-relation among various data points and leverage the same for analysis and compliance. The embodiments of the present disclosure which provide a discovery adapter that can import the data from different tools and data repositories using different protocol. The embodiments of the present disclosure includes one or more benefits such as: (i) Identification of application hotspots, (ii) Prediction of release stability at each stage of development, (iii) Continuous monitoring of release health at each stage of development cycle, (iv) Prediction of release stability in turns reduce the effort and improve time to market, and (v) Reduce problem resolution time (reduced MTTR) for QE issues because of better traceability and visibility.
[058] The embodiments of the present disclosure provides an intelligent analysis and processing of data generated across test lifecycle using well defined and detailed data-map that automatically co-relates and establishes end to end traceability amongst various quality engineering building blocks at multiple levels to provide greater visibility and to take right decision instantaneously to a user. The embodiments of the present disclosure implement a tagging mechanism for

establishing the correlation and end to end traceability across multiple levels. The embodiments of the present disclosure implement a systematic method to generate an algorithm to calculate the release stability index for measuring an application test effectiveness and release quality for decision. The embodiments of the present disclosure is capable to co-relate all the data points in a release context and continuously analyze the data to assess the risk and process maturity.
[059] The embodiments of the present disclosure help to take guided decisions and benchmark test processes and also provides cognitive and contextual insights can also be used to drive the overall test strategy, release plan formulation and their continuous refinements for the larger enterprise. Further, the present disclosure helps in predictive software product delivery, by capturing known issues, risks earlier in the lifecycle thereby optimizing the release pipeline. The present disclosure reduces the risk of software failures that can have broader impact if applications are associated with society. The present approach of considering the empirical data points across the release life cycle, using those data for predicting the release stability and stability index.
[060] The embodiments of the present disclosure include one or more technological advancements such as (a) complex relationships among the parameters of the different stages are being captured using one or more machine-learning algorithms in the implementation, (b) metrics for each stages are used instead of all the particular stage, (c) continuous calculation of the stability is performed as the development is in progress. The overall stability is dependent on computation of parameters at each stage and the complex relationship is captured by the regression algorithm.
[061] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.

[062] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[063] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[064] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are

appropriately performed. Alternatives (including equivalents, extensions,
variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[065] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[066] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

We Claim:
1. A processor implemented method for determining stability of a release of
applications based on a release stability index, comprising:
configuring, via one or more hardware processors, a plurality of data sources to import a relevant data;
extracting, via the one or more hardware processors, a plurality of data from the configured plurality of data sources, wherein the plurality of data corresponds to at least one of: (i) a structured data, (ii) an un-structured data, and combination thereof;
deriving, via the one or more hardware processors, a plurality of rules and a plurality of policies based on the plurality of data;
deriving, via the one or more hardware processors, a release stability model for at least one of plurality of phases of a release management cycle;
learning, via the one or more hardware processors, the derived release stability model to analyze at least one data pattern associated with at least one parameter of the plurality of phases to compute a metrics; and
determining, via the one or more hardware processors, the release stability index based on the derived at least one data pattern to predict the stability of the release of the plurality of applications.
2. The processor implemented method as claimed in claim 1, wherein the relevant data corresponds to a historical and a real-time data to create a knowledge base and a plurality of data models for training.
3. The processor implemented method as claimed in claim 1, wherein the plurality of phases of the release management cycle corresponds to at least one of: (i) plan, (ii) develop, (iii) build, (iv) test, and (v) deploy.
4. The processor implemented method as claimed in claim 1, wherein the release stability model is derived based on at least one of: (a) one or more data patterns observed over time between how the stability of release is impacting based

on an identified impacting attributes, (b) a derived release stability index value, and (c) Rules and respective weightage are applied to different impacting attributes to identify importance or to prioritize a learning based on one or more high impacting attributes.
5. The processor implemented method as claimed in claim 1, wherein the release stability index is calculated using a weighted sum of parameters to determine how much stability achieved by the release of the plurality of applications, wherein a higher value of the release stability index indicate a high stability and a lower value of the release stability index indicate a low stability.
6. A system (100) to determine stability of a release of applications based on a release stability index, comprising:
a memory (102) storing instructions; one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
configure, a plurality of data sources to import a relevant data;
extract, a plurality of data from the configured plurality of data sources, wherein the plurality of data corresponds to at least one of: (i) a structured data, (ii) an un-structured data, and combination thereof;
derive, a plurality of rules and a plurality of policies based on the plurality of data;
derive, a release stability model for at least one of plurality of phases of a release management cycle;
learn, the derived release stability model to analyze at least one data pattern associated with at least one parameter of the plurality of phases to compute a metrics; and

determine, the release stability index based on the derived at least one data pattern to predict the stability of the release of the plurality of applications.
7. The system as claimed in claim 6, wherein the relevant data corresponds to a historical and a real-time data to create a knowledge base and a plurality of data models for training.
8. The system as claimed in claim 6, wherein the plurality of phases of the release management cycle corresponds to at least one of: (i) plan, (ii) develop, (iii) build, (iv) test, and (v) deploy.
9. The system as claimed in claim 6, wherein the release stability model is derived based on at least one of: (a) one or more data patterns observed over time between how the stability of release is impacting based on an identified impacting attributes, (b) a derived release stability index value, and (c) rules and respective weightage are applied to different impacting attributes to identify importance or to prioritize a learning based on one or more high impacting attributes.
10. The system as claimed in claim 6, wherein the release stability index is calculated using a weighted sum of parameters to determine how much stability achieved by the release of the plurality of applications, wherein a higher value of the release stability index indicate a high stability and a lower value of the release stability index indicate a low stability.