Software is designed for humans and must account for their values. However, current research and practice focus on a narrow range of well-explored values, e.g. security, overlooking a more comprehensive perspective. Those exploring a broader array of values rely on manual identification, which is labour-intensive and prone to human bias. Moreover, existing methods offer limited reliability as they fail to explain their findings. In this paper, we propose leveraging the reasoning capabilities of Large Language Models (LLMs) for automated inference about values. This allows for not only detecting values but also explaining how they are expressed in the software. We aim to examine the effectiveness of LLMs, specifically ChatGPT (Chat Generative Pre-Trained Transformer), in automated detection and explanation of values in software artifacts. Using ChatGPT, we investigate how mobile APIs align with human values based on their documentation. Human evaluation of ChatGPT's findings shows a reciprocal shift in understanding values, with both ChatGPT and experts adjusting their assessments through dialogue. While experts recognise ChatGPT's potential for revealing values, emphasis is placed on human involvement to enhance the accuracy of the findings by detecting and eliminating convincing but inaccurate explanations provided by the language model due to potential hallucinations or confabulations.

ABSTRACT Due to the recent advancements in high‐speed networks, underlying hardware computing resources and resource scheduling algorithms, Cloud computing has emerged as a popular computing paradigm globally providing end‐user services such as infrastructure, hardware platforms and application tools. Subsequently, the researchers across various domains have integrated different services to facilitate the end users. However, the real issue faced by the cloud infrastructure is the network latency due to the physical dispersion between clients and cloud data centers. According to an estimate, billions of internet of things (IoT) devices are sharing approximately two exabytes of data daily. Such a huge amount of data can affect network performance if the underlying physical system does not expand up to the required levels, leading to performance degradation. To overcome these issues, a new computing paradigm called Fog Computing has emerged in recent years. In this paper, we discuss the recent developments in fog computing with the integration of real‐time Healthcare 5.0 technology. Furthermore, we describe the proposed layered architecture and taxonomy of resource management (RM) techniques in fog computing, which consists of energy awareness, scheduling, reliability and scalability. Besides that, our survey covers the three‐tier layered architecture, evaluation metrics, real‐time application aspects of fog computing and tools providing the implementation of RM techniques in fog computing. Furthermore, the proposed layered architecture of the standard fog framework and different state‐of‐the‐art techniques for utilising the computing resources of fog networks have been covered in this study. Moreover, we include various sensors to demonstrate the fog data offloading example in healthcare 5.0 applications. We also present a thorough discussion on various current and future real‐time applications of fog computing. Finally, open challenges and promising future research directions have been identified and discussed in the area of fog‐based real‐time applications.

Abstract Sarcasm detection is challenging in natural language processing (NLP) due to its implicit nature, particularly in low‐resource languages. Despite limited linguistic resources, researchers have focused on detecting sarcasm on social media platforms, leading to the development of specialized algorithms and models tailored for Urdu text. Researchers have significantly improved sarcasm detection accuracy by analysing patterns and linguistic cues unique to the language, thereby advancing NLP capabilities in low‐resource languages and facilitating better communication within diverse online communities. This work introduces UrduSarcasmNet, a novel architecture using cascaded group multi‐head attention, which is an innovative deep‐learning approach that employs cascaded group multi‐head attention techniques to enhance effectiveness. By employing a series of attention heads in a cascading manner, our model captures both local and global contexts, facilitating a more comprehensive understanding of the text. Adding a group attention mechanism enables simultaneous consideration of various sub‐topics within the content, thereby enriching the model's effectiveness. The proposed UrduSarcasmNet approach is validated with the Urdu‐sarcastic‐tweets‐dataset (UST) dataset, which has been curated for this purpose. Our experimental results on the UST dataset show that the proposed UrduSarcasmNet framework outperforms the simple‐attention mechanism and other state‐of‐the‐art models. This research significantly enhances natural language processing (NLP) and provides valuable insights for improving sarcasm recognition tools in low‐resource languages like Urdu.

The expeditious improvement in medical imaging technology has been crucial in diagnosing various conditions like cervical spondylosis. However, there is a need for improvement in terms of accuracy and efficiency in the existing models to obtain optimal diagnostic results. This limitation of existing models particularly hampers the resolution and clarity of MRI where there is a need for finer details for the accurate diagnoses of the problem. To limit this gap, our research represents a pioneering approach that merges GAN and spectral clustering. Our research shows the innovative amalgamation of two technologies. The GAN model is enhanced by the sturdy segmentation abilities of spectral clustering, resulting in the significant betterment in diagnosis of problems. This GAN is specifically designed for medical imaging; it consists of a deep convolutional network based on U‐Net architecture. GAN consists of a generator that generates the MRI image through a series of convolutional and deconvolutional layers, and a discriminator checks whether the MRI image is real or generated. This approach not only improves the quality of the image but also leads to a more brisk and accurate diagnosis of cervical spine deformities. The methodology was meticulously tested on diverse datasets, including Medscape, RSNA 2022, and CTSpine1k. The results were remarkable, showing an 8.3% increase in accuracy, 5.5% improvement in precision, 8.5% higher recall, 3.5% greater AUC, 4.9% increased specificity, and a 1.9% reduction in delay compared to the existing classification methods. The influence of this work is profound, providing a consideration spike in the capability of diagnosing problems of cervical spondylosis. By providing improved image resolution and highly precise diagnostic tools, this advancement helps clinicians to make more accurate decisions as well as provides various innovations that help in medical imaging in the future.

Abstract Global software development (GSD) refers to developing software with a distributed team spanning multiple locations and time zones. Based on relationships, there are four types of outsourcing: dyadic (one client–one vendor), multi‐vendor (one client–many vendors), co‐sourcing (many clients–one vendor), and complex outsourcing (many clients–many vendors). Compared to the other types of outsourcing contracts, complex outsourcing contracts are the hardest to work on and have the highest risk of project failure. This paper presents a model, the complex outsourcing relationships management model (CORMM), to assist the complex outsourcing stakeholders (both the clients and vendors) in managing their relationships in the context of GSD. This paper aims to develop a CORMM to assist the complex outsourcing relationships management stakeholders in GSD. Also, we are interested in identifying the applicability and effectiveness of the CORMM in the real‐world industry. The research approach follows a structured methodology comprising multiple phases. Initially, it leverages a systematic literature review (SLR) as its primary research method. The second phase involves the validation of the SLR findings via an empirical study. Subsequently, in the third phase, a model is developed. Finally, the proposed research approach is validated by incorporating two industrial case studies to assess the organization's relationship management utilizing the Motorola tool. The case study results show that CORMM can successfully point out relationship management issues in a complex outsourcing context. The feedback received from the participants of both companies indicates several positive and valuable insights about the CORMM and its application in the context of complex outsourcing relationships. The results highlight that CORMM serves as an assessment tool for evaluating an organization's relationship management capability and a means for organizations to enhance their position. Through CORMM, complex outsourcing organizations (many clients–many vendors) can identify strengths and weaknesses in their relationship management practices, enabling targeted improvement efforts.

This research presents DeepCRINet, a deep learning (DL) model designed for reliable performance across various Chest Radiography Images (CRIs) datasets, in response to the urgent need for quick and accurate lung disease identification utilizing CRIs. Our method builds on earlier research, which frequently used single-source datasets that might not adequately represent the heterogeneity present in clinical situations. Our model’s diagnostic adaptability and real-world dependability are improved by utilizing images from different datasets, which helps us overcome limitations such as dataset bias, robustness, generalizability, and underrepresentation of conditions. With validation on a broad dataset consisting of 14,096 images (from three different datasets), DeepCRINet provides a solution that demonstrates excellent flexibility in recognizing illnesses including TuBerculosis, Pneumonia, COVID-19, and Lung Opacity. Through data augmentation, we improve the dataset, supporting training and testing procedures and confirming the model’s ability to generalize. We used occlusion sensitivity as a kind of explainable AI to openly identify and visually emphasize regions important to proper classification. This ability not only shows that DeepCRINet is analytically better than other DL models and hybrid techniques, but it also improves patient outcomes and diagnosis, which makes it a vital tool for medical professionals like radiologists.

Abstract The emergence of 6G networks initiates significant transformations in the communication technology landscape. Yet, the melding of quantum computing (QC) with 6G networks although promising an array of benefits, particularly in secure communication. Adapting QC into 6G requires a rigorous focus on numerous critical variables. This study aims to identify key variables in secure quantum communication (SQC) in 6G and develop a model for predicting the success probability of 6G-SQC projects. We identified key 6G-SQC variables from existing literature to achieve these objectives and collected training data by conducting a questionnaire survey. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the 6G-SQC matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each 6G-SQC project variable determined using NBC and LR indicated a strong positive correlation (rs = 0.895). The t-test results (t = 0.752, p = 0.502 > 0.05) show no significant differences between the rankings calculated using both prediction models (NBC and LR). The results reveal that the developed success probability prediction model, based on 15 identified 6G-SQC project variables, highlights the areas where practitioners need to focus more to facilitate the cost-effective and successful implementation of 6G-SQC projects.

Summary End‐user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end‐user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high‐star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end‐users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end‐user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end‐users and professionals use to report fake reviews by critically analyzing the end‐user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd‐users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end‐users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end‐user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f‐measures. The accuracy of manually identifying fake reviews by the crowd‐users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.

Abstract Context Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.

In the n-tier framework, data generated by the sensors requires immediate execution. The processing elements need powerful resources to entertain incoming requests. Fog computing, unlike cloud computing, provides low latency for real-time applications. However, data generated by the real-time Internet of Things (IoT) devices significantly impacts the fog devices. The data generated must be processed by the fog devices with quick response time, minimum delay, and energy consumption and send it back to the end-users with high reliability and success rate. However, devices fail due to damage or internal state of a fog device which measures incorrectly or causes destruction which badly affects the overall system performance. The end-to-end transmission requests from the IoT devices require immediate response with minimal delay, execution cost, and energy consumption in spite the occurrence of fog devices failure. In this article, we propose a novel energy efficient task scheduling algorithm based on reactive fault tolerance in an n-tier fog computing framework for IoT applications to enhance the overall fog computing performance. In case of fog device failure, the assigned task is rescheduled to other executable fog nodes without further delay. The proposed framework is based on the modified particle swarm optimization and is designed and evaluated in iFogSim. The main objective of the proposed technique is to reduce energy consumption, latency, network bandwidth utilization, and increase system reliability and success rate. Several experiments have been carried out by taking a maximum of ten iterations based on which it is concluded that the proposed technique reduces energy consumption by 3%, latency by 5%, network bandwidth utilization by 3%, and increases the system reliability by 2% and success rate by 8%.

Abstract In modern times, internationally organized teams face a number of coordination problems owing to their different physical operating locations. These challenges usually come in temporal, cultural, and linguistic forms. To resolve some of these issues, we need more coordination, teamwork, and shared understanding in the requirements engineering phase. Many approaches have been introduced to overcome these challenges associated with global software engineering (GSE). The objective of this research study is to introduce amateurs to GSE and improve their understanding of its associated challenges through an activity‐based learning approach. Our method is primarily targeted toward students who already have theoretical knowledge on the topic but require first‐hand experience with GSE. With the aforementioned motivation in mind, we propose, designe, and empirically evaluate two different activities that can help enhance awareness of GSE challenges. For each activity, we simulate an environment wherein participants are made to go through various constructed coordination challenges related to communication, time management, team mistrust, linguistic barriers, cultural barriers, and distribution of tasks. The effectiveness of our proposed activities, captured by the extent to which participants were able to deal with GSE challenges, was judged through various techniques including (i) observation, (ii) post activities survey questionnaire, and (iii) brainstorming and discussion. We show that the proposed activities were effective in helping students learn and further their understanding of GSE concepts. In particular, discussion sessions and survey questionnaire results reflect their ability to identify critical GSE challenges (specifically related to teams) in a simulated scenario.

Abstract Requirements elicitation is a process that involves gathering requirements for a given project. Several studies have been published suggesting strategies to improve the requirements gathering process. Using game‐based and crowd‐based approaches, researchers are extracting requirements that are useful for product development today. This study follows the same line of research. This research study aims to improve the understanding of the requirements gathering process by novices or students through different activities: (I) knowledge of requirements gathering method and (II) techniques or activities viable for software requirements (education). Important methods used to address the above objectives are as follows: (I) a comprehensive review of the literature to understand requirements gathering; (II) designing an activity to embed RE challenges and RE sub‐activities; and (III) experiment, survey, and observation to collect data and to assess the proposed methods' effectiveness. The suggested activity for requirement gathering is based on the game tic‐tac‐toe. The participants suggest that the design of the activity is helpful in brainstorming and is also valuable for identifying requirements; moreover, a post questionnaire has been designed to determine the learning of the participants regarding the proposed activity. We can observe simply from the coefficients that both skills and challenges (as perceived by the participants) have positive impacts on engagement, immersion, and perceived learning. The proposed activity helps novices or students gain (basic) knowledge of the requirements gathering process/technique; the outlined activity can be a way of learning requirements and gathering knowledge (basic). From this study, we conclude that the proposed activity has positive results and is helpful for participants to get a better understanding of the requirements engineering method(s).

Anomaly detection and behavioral recognition are key research areas widely used to improve human safety. However, in recent times, with the extensive use of surveillance systems and the substantial increase in the volume of recorded scenes, the conventional analysis of categorizing anomalous events has proven to be a difficult task. As a result, machine learning researchers require a smart surveillance system to detect anomalies. This research introduces a robust system for predicting pedestrian anomalies. First, we acquired the crowd data as input from two benchmark datasets (including Avenue and ADOC). Then, different denoising techniques (such as frame conversion, background subtraction, and RGB‐to‐binary image conversion) for unfiltered data are carried out. Second, texton segmentation is performed to identify human subjects from acquired denoised data. Third, we used Gaussian smoothing and crowd clustering to analyze the multiple subjects from the acquired data for further estimations. The next step is to perform feature extraction to multiple abstract cues from the data. These bag of features include periodic motion, shape autocorrelation, and motion direction flow. Then, the abstracted features are mapped into a single vector in order to apply data optimization and mining techniques. Next, we apply the associate‐based mining approach for optimized feature selection. Finally, the resultant vector is served to the k‐ary tree hashing classifier to track normal and abnormal activities in pedestrian crowded scenes.

Abstract Quality of research and knowledge sources can be a critical factor to judge the quality of the research citing them. The prevailing online dissemination of research via blogs, websites, experience reports, and white papers can be helpful as it minimizes potential publication biases. In this paper, we have (i) identified the body of software engineering (SE) literature that is non‐indexed by Web‐of‐Science but has been used in published SE systematic literature review (SLR) studies (as primary studies); (ii) proposed a checklist‐based method for quality assessment of non‐indexed and grey literature; (iii) empirically evaluated the effectiveness of the proposed method; (iv) searched primary studies retrieved from the SLRs in DBLP and Google Scholar (GS) to verify whether DBLP or Google Scholar can act as viable options to search for non‐indexed Web‐of‐Science (non‐WOS‐indexed) literature. Besides proposing our synthesized checklist method, we found that (i) SE literature reviews use significantly non‐indexed Web‐of‐Science sources; (ii) DBLP only covers a portion of the non‐WOS‐indexed reference, whereas GS has a more complete coverage of the non‐indexed reference in comparison. Preliminary evaluation of the proposed quality assessment point system‐based and checklist‐based methods shows it to be a viable way to assess quality of the non‐indexed and grey literature.

Abstract User forums enable a large population of crowd‐users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end‐user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user‐generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd‐based requirements engineering by valuation argumentation (CrowdRE‐VArg) approach that analyzes the end‐users discussion in the Reddit forum and identifies conflict‐free new features, design alternatives, or issues, and reach a rationale‐based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd‐users on the run by finding a settlement that satisfies the involved crowd‐users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE‐VArg approach is illustrated through a sample crowd‐users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE‐VArg approach. The results demonstrate that the proposed CrowdRE‐VArg approach works as a proof‐of‐concept and automatically identifies prioritized requirements‐related information for software engineers.

Abstract Nowadays, data is increasing exponentially, although cloud computing is an eminent approach for the organization, processing, and availability of data for organizational growth over the internet. Besides, a lot of advantages of cloud computing, yet it is suffered from security challenges, which affect big data while using cloud services on the internet. For this purpose, we conducted a detailed systematic literature review (SLR) study, to identify and capture the security challenges of big data on the cloud computing platform. Our research findings determine and develop a taxonomy, based on the prioritization of the security challenges of big data on cloud computing. We identified a total of 15 critical security challenges using the proposed SLR, with the frequency of each challenge >25%, and are further validated by the industrial specialists using a questionnaire survey study. The identified security challenges are data secrecy issue, geographical data location issue, unauthorized data access issue, lack of control, lack of data management, network‐level issues, data integrity issue, data recovery issue, lack of trust, data sharing issue, data availability, asset issues, legal amenabilities, lack of quality issues, and lack of consistency. The security challenges identified and captured through the SLR study are categorized into four levels, namely, steadiness, management, control, and eminence. Conclusively, we applied the fuzzy‐TOPSIS approach to prioritize and identify the significance of each identified security challenge for the big data usage on cloud computing. Based on our proposed approach, the “data secrecy issue” has been identified as the most prominent security challenge with the captured value of “0.765.” The fuzzy TOPSIS is an effective and innovative research approach in the field of computer science. It has been applied positively to other research areas to address and identify the fuzziness and uncertainty of multiple decision‐making glitches. The findings of the research paper will assist the software vendor organization when using the cloud platform for big data security. Also, using the proposed approach, software vendors can prioritize and analyze the uncertainty and ambiguousness among these security challenges.

Online discussion forums can be used for reflecting on the overall user experience of a system. If a user forum is well‐structured, it can be a valuable source of requirements‐related information, which can potentially be accommodated in the requirements engineering process to enhance the current and future software. However, presently, there are limited approaches for extracting such requirements‐related information from the relevant community forums. To fill this gap, this study proposes an automated approach, which automatically identifies requirements information using natural language processing and machine learning. For this purpose, the authors analysed 3319 user comments collected from the seven discussion topics in the Reddit forum. Then, using a content analysis approach, they studied how frequently end‐users submit such information across each discussion topic. Also, they developed an automated approach that identifies key stakeholders, who frequently contribute his rationales in the forum discussion. Further, they employed different machine learning algorithms to classify user comments into rationale elements of different types. The authors' results show that online forums, such as Reddit, can be a rich source of requirements elicitation. Also, machine learning is a promising tool to detect user's rationale and identify different kinds of requirements modelling elements.

Abstract Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during on‐going interactions. In this paper, the authors propose a Crowd‐based Requirements Engineering approach by Argumentation (CrowdRE‐Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting‐viewpoints, reason about the winning‐arguments and present that to systems analysts to make informed‐requirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition‐based meta‐argumentation framework as well as user voting techniques. The CrowdRE‐Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict‐free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict‐free features and issues along with their winning arguments.

In order to sustain, software systems have to evolve in favor of its main target users. Due to the pervasive adoption of online user forums and social media, collecting users feedbacks and comments become possible. However, such crowd generated data are often fragmented, with various viewpoints mentioned during a series of message exchange. The aim of the thesis is to propose an argumentation-based CrowdRE approach, which represents such group conversations as a user argumentation model with the original conversation structure reserved. Based on the argumentation model, we are able to identify new features proposed by the crowd-users or issues encountered, and their supporting and attacking arguments using argumentation theory. To accomplish this research, we adopted an abstract argumentation, bipolar argumentation framework, and coalition-based meta argumentation framework. In addition, to provided automated support to our proposed approach, algorithms will be developed for bipolar argumentation, coalition-based meta argumentation, and end-users voting mechanism. Finally, this thesis employees different machine learning algorithms to automatically classify crowd-users comments into rationale elements and identify conflict-free features or claims based on their supporting and attacking arguments. Initial results show that the proposed approach can identify features, issues and their supporting and attacking arguments with acceptable performance.

In the past, users were asked to express their needs and intentions by writing a structured requirements document in natural language. Due to the pervasive use of online forums and social media, user feedback is more accessible today. However, the information obtained is often fragmented, involving multipleperspectives from multiple parties on an on-going basis. In this paper, we propose a Crowd-based Requirements Engineering approach by Argumentation (CrowdRE-Arg), which analyses the conversations from user forum, identifies the arguments in favor or opposing of a given requirements related discussion topic. By generating the argumentation model of the involved user statements, we are able to recover the conflicting viewpoints, to reason about the winning arguments for informed requirements decisions. The proposed approach is illustrated with a data set of sample conversations about the design of a new Google-Map feature from Reddit. Also, we apply natural language processing techniques and machine learning algorithms to support the automated execution of the CrowdRE-Arg approach.