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 [1]. 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 [2]. 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 [3] 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 [2]. Many organizations take a strategic approach to make vs. buy decisions [4]. 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 [5]. 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 [2].

In the development of software in SMEs, the build versus buy decision might take any form, as shown in Fig. 1. The pathways of software build vs. buy decision are depicted in Fig. 1. Build vs. buy is a difficult decision to make because it involves many factors such as capacity, organizational resources, sufficient time to be available, and the presence of required expertise in the organization; if all of these factors are present, the decision will be in favor of build; otherwise, an alternative approach will be considered. We gathered the elements from the literature and then updated them with expert opinions gleaned from social media.

Social Media are a popular platform to discuss and experience things in a different way. Not only it brings change into someone’s opinion but also becomes the cause of social change [6]. Twitter has risen as a broadly used microblogging service. It has a huge and rapidly rising user. They post status (messages) called tweets. It is a short message with a length of 140 characters [7] initially which has upgraded the character length now. Hence, Twitter data is also used as the base to classify Build vs. Buy. Classification is the categorization of data into one or more groups. Data is classified into two categories as labeled and unlabelled data. Unlabelled data is labeled into some classes by using machine learning algorithms. Commonly there are two types: classic binary and modern multiclass [8]. Multiple tools are used for the classification of data such as WEKA. WEKA stands for “Waikato Environment for Knowledge Analysis” [9]. It has many built-in features like feature generation, feature selection, classification, and evaluation of the selected model. Moreover, the result of the classifier depends on the nature of the data. Therefore, the objective of this research is to identify the factors that help organizations to take an appropriate decision either software should be in-house or buy from vendors.

The organization of the study is as follows. Section 2 elaborates the related work from literature, section 3 discusses the methodology adopted. Section 4 elaborates on the results of the study whereas, the conclusion and future work is presented in section 5.

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 [10]. IT can be utilized to recognize and find people with specific expertise nevertheless of a person’s physical location [11]. 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 [12]. 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 [13]. Commercial-off-the-shelf (COTS) may not fully fit all requirements [14]. According to Xu [15], 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. Cortellessa [16] 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) [17]. According to Daneshgar et al. [18], 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 [19,20]. 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 [20] 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 [21]. 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 [2,18] 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. 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 [22]. With some limit’s tweets can be searched by API, a search facility provided by Twitter [23]. Per day production of tweets is half of a billion by its users [24]. It is a faster way to gain knowledge. Tweet data is collected and data is divided into two classes; yes, and no.

The literature gives rise to different questions like how to build or buy decisions should be taken for different organizations? What are the factors that affect the organization’s development method? and what are the reasons behind the build vs. buy decision? Therefore, the objective of this research is to identify the factors that help organizations to take an appropriate decision either software should be in-house built or buy from vendors. Another objective is to suggest an appropriate decision to build vs. buy in SMEs with the help of a framework. Machine learning classification is used in this study to classify data into two categories i.e., build & buy.

In the literature study, the most significant factors studied by many researchers are “Time” and “Risk,” followed by economics, cost, complexity, and changeability. The variables “strategy and competitive advantage,” “support structure,” and “operational factor” are the ones that have received the least attention.

To make the study more complete, a mixed-method approach based on qualitative and quantitative methodologies is used. This research tries to determine the best decision for businesses to build vs. buy based on the characteristics listed in Tab. 1 and gleaned through a literature evaluation.

The methodology adopted in this study is based on four steps.i.

First, factors influencing the decision to make vs. buy are found in the literature, with a total of twelve factors indicated in Tab. 1.

Data collection is the key part of these types of studies. Numerous ways are used for data pool such as interviews, surveys, meetings, and an online survey. The collection of data is a crucial requirement to establish the cogency of this approach. For the survey, data is collected by digital means. It is conceivable to compute the reliability and the capability of summing up results to a bigger population. Furthermore, the profundity of a quantitative report is restricted [28]. The online questionnaire is sent to 65 organizations in Pakistan among which 58 had responded. 10 responses are not filled while 2 are not filled by relevant persons. Thus, valid data of 46 organizations are utilized in this study. The survey consists of a five-point Likert scale i.e., from 0-strongly disagree, to 4-strongly agree is applied to get responses from experts.

For the experts’ interview phase, face to face interview session of 30 min for each interviewee is conducted. Interviewees are asked five open-ended questions regarding build vs. buy decisions in their organizations. The findings of the interviews are summarized and analyzed for common themes.

For this study, 569 Tweeter tweets are collated are classified. All tweets of IT experts (known) from 2009 to the present are collected during the two months. Tweets collection is an essential and basic phase of this approach therefore by using API search, tweets are collected from Twitter. The pre-processing technique for withdrawal of inappropriate characters and elimination of other frequent words is applied. All data is pre-processed before giving it to classifiers. Furthermore, Tweets Data, Outlier Removal, Keyword Stemming, Spelling Correction, and Category Ranking steps have been performed. After the execution of a classifier, the next stage is the validation of the model by a cross-validation method. There are multiple techniques used for this purpose. Precision, accuracy level, recall, and F-measure are frequently utilized [22]. Recall and precision are inversely proportional to each other; when recall increases precision decreases and when recall decreases precision increases [23].

The outcomes of the survey’s data analytics with SPSS, interview sessions, and Tweeter data analysis using machine learning algorithms are discussed in this section.

For a long time, commercial of the shelf is available for organizations of all types, however, the problem is the right choice of ‘build versus buy’. All those organizations are facing difficulty in taking the right decision due to which a deep understanding of the system is required to find out core factors on which the decision to build or buy should be taken. In this research, we tried to help organizations in taking accurate decisions for software development. We used both qualitative and quantitative approaches to get the most appropriate results. Firstly, 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. Furthermore, 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.

This 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.