# Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development
###### Abstract  
Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failure risk. Software development wastes stakeholders' time and effort as they cannot find a suitable solution in a highly dynamic and competitive marketplace. It is, therefore, crucial to automatically predict the success of an upcoming software project before crowdsourcing it. This will save stakeholders' and co-pilots' time and effort. To this end, this paper proposes a well-known deep learning model called Bidirectional Encoder Representations from Transformers (BERT) for the success prediction of Crowdsourced Software Projects (CSPs). The proposed model is trained and tested using the history data of CSPs collected from _TopCoder_ using its _REST API_. The outcomes of hold-out validation indicate a notable enhancement in the proposed approach compared to existing methods, with increases of 13.46%, 8.83%, and 11.13% in precision, recall, and F1 score, respectively. Competitive Crowdsourced Software Development (CCSD); TopCoder; BERT; classification 20241489. 1489. Projects are split into multiple tasks and published on these platforms for the world's distributed software community to propose innovative solutions in exchange for monetary rewards [5]. However, since this community is largely unseen, scattered, and uncontrollable, the success of CCSD depends on the participant's willingness to engage in these competitions [6]. CCSD's success depends heavily on the excitement of the software community as the quality and amount of resources given by the community is determined by it [7; 8; 9; 10]. To get the community to help with tasks, they are given rewards like money or social perks [8]. Lots of researchers [7; 11; 12] have looked into why crowds help out, studying how motivation and social ideas affect crowdsourcing. Online employment platforms and crowdsourcing competitions have grown rapidly in popularity [13]. However, research shows a shortage of competitive crowd-workers for software development compared to market needs [14]. Consequently, crowdsourcing websites like TopCoder may provide low-quality and less inventive software products to requesters [15]. TopCoder has a high submission failure review ratio of 35.4% [6], leading to a significant portion of software development tasks being canceled or unsatisfactory. This highlights the risk factors for failure linked to motivating aspects, such as monetary rewards [16; 17]. CCSD is in its early stages and faces challenges, i.e., low submission rates and a high risk of project failure [18]. This results in project cancellation, unsatisfactory solutions [15], and wasting stakeholders' time and effort [19]. The CCSD environment, characterized by unknown factors, a geographically scattered crowd of developers, and limited visibility and control over project progress, contributes to the platform's limited predictability [16]. This unpredictability results in client dissatisfaction and negatively affects CCSD platforms' reputation [15; 20]. Therefore, it is imperative to predict the success of CSPs. To predict CSP success automatically, some numerical representations of the text and classification models have been used [4]. Different M/DL-based strategies are successful in solving different issues in the CCSD domain. For example, recommending the appropriate developer for the required software development project [21; 22], investigating the developer's history [23; 24], figuring out other contributing success factors of the CCSD projects [14; 25], developing simulation methods for failure prediction and task scheduling [26; 27], success prediction in the CCSD [4], and quality assessment [28; 29]. Authors [30; 31] provided significant contributions to this field, specifically in the context of using deep learning for project success prediction.