A SYSTEM AND APPLICATION FOR A MIXED-METHOD APPROACH TO MAKE VS BUY DECISION SOFTWARE

Abstract

ABSTRACT A SYSTEM AND APPLICATION FOR A MIXED-METHOD APPROACH TO MAKE VS BUY DECISION SOFTWARE Over the past few decades, multiple software development process models, tools, and techniques have been used by practitioners. Despite using these techniques, most software development organizations still fail to meet customer’s needs within time and budget. Time overrun is one of the major reasons for project failure. There is a need to come up with a comprehensive solution that would increase the chances of project success. However, the “make vs. buy” decision can be helpful for “in time” software development. Social media have become a popular platform for discussion of all sorts of topics, so software development is no exception. Software developers discuss all the pros and cons of making vs. buy decisions on Twitter and other social media platforms. Twitter trending is a typical feature that evaluates the level of popularity of a specific event on online networking. A mixed-method approach comprising of interviews of software industry experts and Twitter data extraction is applied to scrutinize the effective decision of software build vs. buy decision. The findings of the analysis show that software makes vs. buy decisions depend on several factors including cost, development technology, software development team skills, and time. Based on the finding of the study a framework is proposed for the decision to build versus buy in Small and medium-sized enterprises (SMEs). Furthermore, the framework has been designed to statistically indicate make versus buy decisions of the organization and to suggest appropriate choices based on different parameters.

Specification

Description:A SYSTEM AND APPLICATION FOR A MIXED-METHOD APPROACH TO MAKE VS BUY DECISION SOFTWARE
FIELD OF INVENTION
[001] The present invention relates to the make vs buy decision software that proposed framework is based on identified factors. The researchers can apply the identified elements in their research. In the future, the approach could aid major organizations in deciding whether to build or buy. The biasness can be handled by using data collected from various social media platforms. Furthermore, the tweets covering images can also be taken under observation. the framework has been designed to statistically indicate make versus buy decisions of the organization and to suggest appropriate choices based on different parameters.


BACKGROUND OF INVENTION
[002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[003] Previously, software tasks are considered as sequences of codes. Single person interpreted and solved all problems of obligatory software where no team management is needed. IT can be utilized to recognize and find people with specific expertise nevertheless of a person's physical location. To write the code people started hiring others as "developers" particularly in modern programming languages. Developers levels are different and hence the code may contain different types of vulnerabilities. The level of developer from given source code is detected by. Moreover, new concepts of some different roles in software development are introduced by the industry i.e., users and developers. Many organizations take a strategic approach to make vs. buy decisions. Organizations that did not see the build/buy choice as an open door tend to use points of reference. Commercial-off-the-shelf (COTS) may not fully fit all requirements. According to Xu, outsourcing is entirely over an in-house generation if the yield of seller's production is adequately low or its economies of the extension are remarkably appealing.

[004] Cortellessa divided the requirements into two parts; functional and non-functional for components. Some companies take the decision of make, buy and make or buy separately on four different theories that are used to elaborate the decision of make or buy for companies (transaction cost economics composite, neoclassical economics/rational choice decision, the resource-based view of strategy and institutional theory explanations). According to Daneshgar et al, discoveries make the required decision more time-consuming. Simply they acknowledged that if dealer support is insufficient, the decision to buy is mostly among SMEs. Cost is always a big consideration; its risk remains the same in both cases. Availability of multiple solutions to requirement fit leads to buying in many cases. Instead of the typical software development approach build vs. buy decision needs the contribution of people (networks, data centers, etc.) that may gather data from different techniques by Torrecilla-Salinas to explore that the agile techniques enrich the build vs. buy decision. The organization mostly goes for a third option except for build vs. buy but to cope with the risk factor. In the process of literature review, multiple factors have been identified that affect the organization's development method. Factors that affect the build vs. buy decision of SEMs are summarized in Tab. 1. Previous research has backed up these factors, which have been used to create a generic set of criteria. Machine learning (ML) is a branch of artificial intelligence. It works on the two approaches supervised and unsupervised learning. In supervised learning provided output is used for the training of the input data.

[005] For the algorithm, a training labeled data set has been used under supervised learning. Different supervised techniques are support vector machine (SVM), naïve Bayes (NB), Logistic regression, Decision tree, and Maximum Entropy. Clustering is an absolute unsupervised learning methodology. Some unsupervised algorithms are HMM, K-Mean, and Neural Network. Classification of data is necessary to acquire desirable results. Commonly there are two types, classic binary, and modern multi-class. Social media, nowadays, has numerous impacts on society. Many social sites are famous including Facebook, Instagram, Viber, WhatsApp, Twitter, Linked In, and many more. Twitter as a social networking site, started on 21 March 2006. Through tweets, people can express their views and emotions precisely due to limited character space, which is 140 in numbers now its 280-character space. With some limit's tweets can be searched by API, a search facility provided by Twitter.

[006] Further limitations and disadvantages of conventional approaches will become apparent to one of skill in the art through comparison of described systems with some aspects of the present disclosure, as outlined in the remainder of the present application and concerning the drawings. As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context dictates otherwise.

[007] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any or a combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.

SUMMARY OF INVENTION
[008] The present invention relates to the make vs buy decision software that proposed framework is based on identified factors. The researchers can apply the identified elements in their research. In the future, the approach could aid major organizations in deciding whether to build or buy. The biasness can be handled by using data collected from various social media platforms. Furthermore, the tweets covering images can also be taken under observation. the framework has been designed to statistically indicate make versus buy decisions of the organization and to suggest appropriate choices based on different parameters.

[009] Software development is one of the leading industries among other commercial industries. Although the software has been developed for the past many decades, however, the development process still needs improvements to predict its faultless development. A big consideration in software development is the decision to "build versus buy". Particularly, commercial off the shelf is available for organizations of all types however all those organizations are facing difficulty in taking the right decision. Previously, researchers have made arguments that support one approach over the other, while some are acknowledged as realities. Because of advancements in the software industry, few of the arguments have become unsubstantial. Software leaders have questioned whether it is better to make software in-house or buy from the market. SMEs (Small and Medium Enterprises) play a very significant role in the development of any country because of having a global impact on the GDP. Smaller-scale enterprises focus on maintaining viability, rise in profits, and increases in sales. A big consideration in development for the organization is the decision to build vs. buy. Build implies that custom subsystems will be built from its core essential parts, then integrates into a final product. Buy implies buying the subsystems and maintaining distance from customizing tasks. Many organizations take a strategic approach to make vs. buy decisions. Organizations that do not consider the build/buy option as an open door are more likely to employ sources of reference where social values based on expert peer acceptance are typically dominated. When a business requires software, it has the option of building it or acquiring it. The decision to acquire more can be made in two ways: whether to buy the entire solution or just a few components. Modifiable off-the-shelf (MoTS) and Commercial off-the-shelf (COTS) solutions are available (CoTS). If the decision is made to construct software, the appropriate environment for software development must be provided. However, deciding whether to build or buy is not a simple decision that takes the careful evaluation of facts, understanding of the organization's strengths and shortcomings, economic situation, and other parameters. Based on the findings of the survey analysis and machine learning classification a framework for the build vs. buys decision. Expertise is drawn from a variety of fields in the proposed framework, including programming, database management, networking, and Android. A specific amount of time is allotted to accomplish a project or meet deadlines; finance includes resources (skilled labor costs, equipment costs, etc.); and capacity includes infrastructure, personnel, and machinery The greater the expertise within an organization, the more the chances of developing, and the lower the knowledge within an organization, the higher the possibilities of buying. More situational elements (time, cost budget) increase the likelihood of building, while fewer elements increase the likelihood of buying. If an organization's resources and capacity are limited, the chances of a build are low; conversely, if the organization's capacity is high, the probabilities of a build are higher. When a new project comes in, the framework shows that the organization checks to see if it has enough competence. The project is denied if the requisite expertise is not present inside the organization. Also, the organization's budget and capacity are examined, and if all of the categories are greater, the organization should build rather than buy.
[0010] Further limitations and disadvantages of conventional approaches will become apparent to one of skill in the art through comparison of described systems with some aspects of the present disclosure, as outlined in the remainder of the present application and concerning the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0012] Figure 1. A proposed framework for the build vs. buy decision

DETAILED DESCRIPTION
[0013] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0014] Figure 1. A proposed framework for the build vs. buy decision. Based on the findings of the survey analysis and machine learning classification a framework for the build vs. buys decision is prosed as shown in Fig. 6 Expertise is drawn from a variety of fields in the proposed framework, including programming, database management, networking, and Android. A specific amount of time is allotted to accomplish a project or meet deadlines; finance includes resources (skilled labor costs, equipment costs, etc.); and capacity includes infrastructure, personnel, and machinery.

[0015] The greater the expertise within an organization, the more the chances of developing, and the lower the knowledge within an organization, the higher the possibilities of buying. More situational elements (time, cost budget) increase the likelihood of building, while fewer elements increase the likelihood of buying. If an organization's resources and capacity are limited, the chances of a build are low; conversely, if the organization's capacity is high, the probabilities of a build are higher. When a new project comes in, the framework shows that the organization checks to see if it has enough competence. The project is denied if the requisite expertise is not present inside the organization. Also, the organization's budget and capacity are examined, and if all of the categories are greater, the organization should build rather than buy

[0016] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow.

[0017] The invention is not limited to the described embodiments, versions, or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person.
, Claims:CLAIMS
We claim,
1. A system and application for a mixed-method approach to make vs buy decision software comprises mixed-method approach comprising of interviews of software industry experts and Twitter data extraction is applied to scrutinize the effective decision of software build vs. buy decision. The findings of the analysis show that software makes vs. buy decisions depend on several factors including cost, development technology, software development team skills, and time. Based on the finding of the study a framework is proposed for the decision to build versus buy in Small and medium-sized enterprises (SMEs).
2. The invention claimed in claim 1, the framework has been designed to statistically indicate make versus buy decisions of the organization and to suggest appropriate choices based on different parameters.
3. The invention claimed in claim 1, core factors are identified on which the decision depends. It shows that the capacity and situational factors (time, cost, and budget) are directly proportional, higher these factors best to build in-house and vice versa.
4. The invention claimed in claim 1, organizational expertise is indirectly proportional to buy decisions, higher than the expertise fewer chances of buy and lower the expertise more chances of buy. Secondly, the context-based analysis of Twitter data is performed. Different machine learning algorithms have been used to get the finest results from Twitter data. Data are gathered about the Make vs. Buy decision. Dataset is passed through two stages (training and testing) to attain the best result from tweet data. A close examination of different machine learning classifiers has been done. Bayes Net and ZeroR have shown the best results inaccuracy.
5. The invention claimed in claim 1, the study is conducted on a limited number of SME organizations and the proposed framework is based on identified factors. The researchers can apply the identified elements in their research. In the future, the approach could aid major organizations in deciding whether to build or buy. The biasness can be handled by using data collected from various social media platforms. Furthermore, the tweets covering images can also be taken under observation.

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