Search Results (4)

Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. Therefore, it is of paramount importance to hear from the interested audience about their desires and requirements in mobile apps. This has stressed the need to employ crowdsourcing in requirements engineering (RE) activities to harness the scattered talent in the crowd. RE tasks require certain software domain knowledge, hence, selecting a suitable subset of the crowd is crucial to obtain high-quality contributions. For that, we propose a crowd selection approach for crowdsourcing mobile app requirements engineering tasks which leverages the untapped crowd available on the social network Twitter (recently changed to X). This article is an extension of our previous work, where we present the proposed social-network-based crowd selection approach design, continue to work on the remaining component of the approach and evaluate the approach through a controlled experiment. For evaluation, the approach was utilized to select a real crowd that were invited to contribute to crowdsourcing requirements elicitation tasks for a fitness mobile app. The quality of the crowdsourced requirements was assessed by experts and the results have provided encouraging and compelling insights about the effectiveness of the proposed approach. The obtained assessment scores for the five quality factors clarity, creativity, relatedness, feasibility and diversity were respectively 4.36, 4.01, 4.29, 4.45 and 4.43 out of 5. Overall, we believe that the proposed social-network-based crowd selection approach could help in eliciting mobile app requirements and features that could cater to the needs of a large audience. Full article

In the last fifteen years, an immense expansion has been witnessed in mobile app usage and production. The intense competition in the tech sector and also the rapidly and constantly evolving user requirements have led to increased burden on mobile app creators. Nowadays, fulfilling users’ expectations cannot be readily achieved and new and unconventional approaches are needed to permit an interested crowd of users to contribute in the introduction of creative mobile apps. Indeed, users and developers of mobile apps are the most influential candidates to engage in any of the requirements engineering activities. The place where both can best be found is on Twitter, one of the most widely used social media platforms. More interestingly, Twitter is considered as a fertile ground for textual content generated by the crowd that can assist in building robust predictive classification models using machine learning (ML) and natural language processing (NLP) techniques. Therefore, in this study, we have built two classification models that can identify mobile apps users and developers using tweets. A thorough empirical comparison of different feature extraction techniques and machine learning classification algorithms were experimented with to find the best-performing mobile app user and developer classifiers. The results revealed that for mobile app user classification, the highest accuracy achieved was ≈0.86, produced via logistic regression (LR) using Term Frequency Inverse Document Frequency (TF-IDF) with N-gram (unigram, bigram and trigram), and the highest precision was ≈0.86, produced via LR using Bag-of-Words (BOW) with N-gram (unigram and bigram). On the other hand, for mobile app developer classification, the highest accuracy achieved was ≈0.87, produced by random forest (RF) using BOW with N-gram (unigram and bigram), and the highest precision was ≈0.88, produced by multi-layer perception neural network (MLP NN) using BERTweet for feature extraction. According to the results, we believe that the developed classification models are efficient and can assist in identifying mobile app users and developers from tweets. Moreover, we envision that our models can be harnessed as a crowd selection approach for crowdsourcing requirements engineering activities to enhance and design inventive and satisfying mobile apps. Full article

The evaluation of earthquake damage is central to assessing its severity and damage characteristics. However, the methods of assessment encounter difficulties concerning the subjective judgments and interpretation of the evaluators. Thus, it is mainly geologists, seismologists, and engineers who perform this exhausting task. Here, we explore whether an evaluation made by semiskilled people and by the crowd is equivalent to the experts’ opinions and, thus, can be harnessed as part of the process. Therefore, we conducted surveys in which a cohort of graduate students studying natural hazards (n = 44) and an online crowd (n = 610) were asked to evaluate the level of severity of earthquake damage. The two outcome datasets were then compared with the evaluation made by two of the present authors, who are considered experts in the field. Interestingly, the evaluations of both the semiskilled cohort and the crowd were found to be fairly similar to those of the experts, thus suggesting that they can provide an interpretation close enough to an expert’s opinion on the severity level of earthquake damage. Such an understanding may indicate that although our analysis is preliminary and requires more case studies for this to be verified, there is vast potential encapsulated in crowd-sourced opinion on simple earthquake-related damage, especially if a large amount of data is to be handled. Full article

Crowdsourced software engineering (CSE) is an emerging area that has been gaining much attention in the last few years. It refers to the use of crowdsourcing techniques in software engineering activities, including requirements engineering, implementation, design, testing, and verification. CSE is an alternative to traditional software engineering and uses an open call to which online developers can respond to and obtain work on various tasks, as opposed to the assigning of tasks to in-house developers. The great benefits of CSE have attracted the attention of many researchers, and many studies have recently been carried out in the field. This research aims to analyze publications on CSE using social network analysis (SNA). A total of 509 CSE publications from six popular databases were analyzed to determine the characteristics of the collaborative networks of co-authorship of the research (i.e., the co-authors, institutions involved in co-authorship, and countries involved in co-authorship) and of the citation networks on which the publications of the studies are listed. The findings help identify CSE research productivity, trends, performances, community structures, and relationships between various collaborative patterns to provide a more complete picture of CSE research. Full article