Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Software Engineering
Title: Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework
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.
Extracted statement — Utilities in CrowdRE: Crowd
Research field: Software Engineering
Title: A gradual approach to crowd-based requirements engineering: The case of conference online social networks
Abstract: This paper proposes a gradual approach to crowd-based requirements engineering (RE) for supporting the establishment of a more engaged crowd, hence, mitigating the low involvement risk in crowd-based RE. Our approach advocates involving micro-crowds (MCs), where in each micro-crowd, the population is relatively cohesive and familiar with each other. Using this approach, the evolving product is developed iteratively. At each iteration, a new MC can join the already established crowd to enhance the requirements for the next version, while adding terminology to an evolving folksonomy. We are currently using this approach in an on-going research project to develop an online social network (OSN) for academic researchers that will facilitate discussions and knowledge sharing around conferences.
Extracted statement — Utilities in CrowdRE: Crowd
Research field: Software Engineering
Title: Discovering Requirements through Goal-Driven Process Mining
Abstract: Software systems are designed to support their users in performing tasks that are parts of more general processes. Unfortunately, software designers often make invalid assumptions about the users' processes and therefore about the requirements to support such processes. Eliciting and validating such assumptions through manual means (e.g., through observations, interviews, and workshops) is expensive, time-consuming, and may fail to identify the users' real processes. Using process mining may reduce these problems by automating the monitoring and discovery of the actual processes followed by a crowd of users. The Crowd provides an opportunity to involve diverse groups of users to interact with a system and conduct their intended processes. This implicit feedback in the form of discovered processes can then be used to modify the existing system's functionalities and ensure whether or not a software product is used as initially designed. In addition, the analysis of user-system interactions may reveal lacking functionalities and quality issues. These ideas are illustrated on the GreenSoft personal energy management system.
Extracted statement — Utilities in CrowdRE: Crowd
Research field: Software Engineering
Title: UCFrame: A Use Case Framework for Crowd-Centric Requirement Acquisition
Abstract: To build needed mobile applications in specific domains, requirements should be collected and analyzed in holistic approach. However, resource is limited for small vendor groups to perform holistic requirement acquisition and elicitation. The rise of crowdsourcing and crowdfunding gives small vendor groups new opportunities to build needed mobile applications for the crowd. By finding prior stakeholders and gathering requirements effectively from the crowd, mobile application projects can establish sound foundation in early phase of software process. Therefore, integration of crowd-based requirement engineering into software process is important for small vendor groups. Conventional requirement acquisition and elicitation methods are analyst-centric. Very little discussion is in adapting requirement acquisition tools for crowdcentric context. In this study, several tool features of use case documentation are revised in crowd-centric context. These features constitute a use case-based framework, called UCFrame, for crowd-centric requirement acquisition. An instantiation of UCFrame is also presented to demonstrate the effectiveness of UCFrame in collecting crowd requirements for building two mobile applications.
Extracted statement — Utilities in CrowdRE: Crowd
Research field: Software Engineering
Title: Mining Context-Aware User Requirements from Crowd Contributed Mobile Data
Abstract: Internetware is required to respond quickly to emergent user requirements or requirements changes by providing application upgrade or making context-aware recommendations. As user requirements in Internet computing environment are often changing fast and new requirements emerge more and more in a creative way, traditional requirements engineering approaches based on requirements elicitation and analysis cannot ensure the quick response of Internetware. In this paper, we propose an approach for mining context-aware user requirements from crowd contributed mobile data. The approach captures behavior records contributed by a crowd of mobile users and automatically mines context-aware user behavior patterns (i.e., when, where and under what conditions users require a specific service) from them using Apriori-M algorithm. Based on the mined user behaviors, emergent requirements or requirements changes can be inferred from the mined user behavior patterns and solutions that satisfy the requirements can be recommended to users. To evaluate the proposed approach, we conduct an experimental study and show the effectiveness of the requirements mining approach.
Extracted statement — Utilities in CrowdRE: Crowd
Research field: Software Engineering
Title: Linguistic Analysis of Crowd Requirements: An Experimental Study
Abstract: Users of today's online software services are often diversified and distributed, whose needs are hard to elicit using conventional RE approaches. As a consequence, crowd-based, data intensive requirements engineering approaches are considered important. In this paper, we have conducted an experimental study on a dataset of 2,966 requirements statements to evaluate the performance of three text clustering algorithms. The purpose of the study is to aggregate similar requirement statements suggested by the crowd users, and also to identify domain objects and operations, as well as required features from the given requirements statements dataset. The experimental results are then cross-checked with original tags provided by data providers for validation.
Extracted statement — Utilities in CrowdRE: Crowd
Research field: Software Engineering
Title: Crowdsourcing to elicit requirements for MyERP application
Abstract: Crowdsourcing is an emerging method to collect requirements for software systems. Applications seeking global acceptance need to meet the expectations of a wide range of users. Collecting requirements and arriving at consensus with a wide range of users is difficult using traditional method of requirements elicitation. This paper presents crowdsourcing based approach for German medium-size software company MyERP that might help the company to get access to requirements from non-German customers. We present the tasks involved in the proposed solution that would help the company meet the goal of eliciting requirements at a fast pace with non-German customers.
Extracted statement — RE activities with crowd involvement: Elicitation
Research field: Software Engineering
Title: The Crowd in Requirements Engineering: The Landscape and Challenges
Abstract: Crowd-based requirements engineering (CrowdRE) could significantly change RE. Performing RE activities such as elicitation with the crowd of stakeholders turns RE into a participatory effort, leads to more accurate requirements, and ultimately boosts software quality. Although any stakeholder in the crowd can contribute, CrowdRE emphasizes one stakeholder group whose role is often trivialized: users. CrowdRE empowers the management of requirements, such as their prioritization and segmentation, in a dynamic, evolved style through collecting and harnessing a continuous flow of user feedback and monitoring data on the usage context. To analyze the large amount of data obtained from the crowd, automated approaches are key. This article presents current research topics in CrowdRE; discusses the benefits, challenges, and lessons learned from projects and experiments; and assesses how to apply the methods and tools in industrial contexts. This article is part of a special issue on Crowdsourcing for Software Engineering.
Extracted statement — RE activities with crowd involvement: Elicitation
Research field: Software Engineering
Title: CRAFT: A Crowd-Annotated Feedback Technique
Abstract: The ever increasing accessibility of the web for the crowd offered by various electronic devices such as smartphones has facilitated the communication of the needs, ideas, and wishes of millions of stakeholders. To cater for the scale of this input and reduce the overhead of manual elicitation methods, data mining and text mining techniques have been utilised to automatically capture and categorise this stream of feedback, which is also used, amongst other things, by stakeholders to communicate their requirements to software developers. Such techniques, however, fall short of identifying some of the peculiarities and idiosyncrasies of the natural language that people use colloquially. This paper proposes CRAFT, a technique that utilises the power of the crowd to support richer, more powerful text mining by enabling the crowd to categorise and annotate feedback through a context menu. This, in turn, helps requirements engineers to better identify user requirements within such feedback. This paper presents the theoretical foundations as well as the initial evaluation of this crowd-based feedback annotation technique for requirements identification.
Extracted statement — RE activities with crowd involvement: Elicitation
Research field: Software Engineering
Title: UCFrame: A Use Case Framework for Crowd-Centric Requirement Acquisition
Abstract: To build needed mobile applications in specific domains, requirements should be collected and analyzed in holistic approach. However, resource is limited for small vendor groups to perform holistic requirement acquisition and elicitation. The rise of crowdsourcing and crowdfunding gives small vendor groups new opportunities to build needed mobile applications for the crowd. By finding prior stakeholders and gathering requirements effectively from the crowd, mobile application projects can establish sound foundation in early phase of software process. Therefore, integration of crowd-based requirement engineering into software process is important for small vendor groups. Conventional requirement acquisition and elicitation methods are analyst-centric. Very little discussion is in adapting requirement acquisition tools for crowdcentric context. In this study, several tool features of use case documentation are revised in crowd-centric context. These features constitute a use case-based framework, called UCFrame, for crowd-centric requirement acquisition. An instantiation of UCFrame is also presented to demonstrate the effectiveness of UCFrame in collecting crowd requirements for building two mobile applications.
Extracted statement — RE activities with crowd involvement: Elicitation
Research field: Software Engineering
Title: Mining Context-Aware User Requirements from Crowd Contributed Mobile Data
Abstract: Internetware is required to respond quickly to emergent user requirements or requirements changes by providing application upgrade or making context-aware recommendations. As user requirements in Internet computing environment are often changing fast and new requirements emerge more and more in a creative way, traditional requirements engineering approaches based on requirements elicitation and analysis cannot ensure the quick response of Internetware. In this paper, we propose an approach for mining context-aware user requirements from crowd contributed mobile data. The approach captures behavior records contributed by a crowd of mobile users and automatically mines context-aware user behavior patterns (i.e., when, where and under what conditions users require a specific service) from them using Apriori-M algorithm. Based on the mined user behaviors, emergent requirements or requirements changes can be inferred from the mined user behavior patterns and solutions that satisfy the requirements can be recommended to users. To evaluate the proposed approach, we conduct an experimental study and show the effectiveness of the requirements mining approach.
Extracted statement — RE activities with crowd involvement: Elicitation
Research field: Software Engineering
Title: Customer Rating Reactions Can Be Predicted Purely using App Features
Abstract: In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89% of these apps can be predicted with 100% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for requirements engineering in app stores: They indicate that app developers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers.
Extracted statement — RE activities with crowd involvement: Elicitation
Research field: Software Engineering
Title: A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution
Abstract: Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number---in the range of thousands per day for popular applications---a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.
Extracted statement — RE activities with crowd involvement: Evolution
Research field: Software Engineering
Title: Software Architecture Optimization Methods: A Systematic Literature Review
Abstract: Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.
Extracted statement — contains: Methods
Research field: Software Engineering
Title: The Crowd in Requirements Engineering: The Landscape and Challenges
Abstract: Crowd-based requirements engineering (CrowdRE) could significantly change RE. Performing RE activities such as elicitation with the crowd of stakeholders turns RE into a participatory effort, leads to more accurate requirements, and ultimately boosts software quality. Although any stakeholder in the crowd can contribute, CrowdRE emphasizes one stakeholder group whose role is often trivialized: users. CrowdRE empowers the management of requirements, such as their prioritization and segmentation, in a dynamic, evolved style through collecting and harnessing a continuous flow of user feedback and monitoring data on the usage context. To analyze the large amount of data obtained from the crowd, automated approaches are key. This article presents current research topics in CrowdRE; discusses the benefits, challenges, and lessons learned from projects and experiments; and assesses how to apply the methods and tools in industrial contexts. This article is part of a special issue on Crowdsourcing for Software Engineering.
Extracted statement — RE activities with crowd involvement: Prioritization
Research field: Software Engineering
Title: Metrics Based Verification and Validation Maturity Model (MB-V2M2)
Abstract: Verification and validation (V&V) is only marginally addressed in software process improvement models like CMM and CMMI. A roadmap for the establishment of a sound verification and validation process in software development organizations is badly needed. This paper presents a basis for a roadmap; it describes a framework for improvement of the V&V process, based on the Testing Maturity Model (TMM), but with considerable enhancements. The model, tentatively named MB-V/sup 2/M/sup 2/ (Metrics Based Verification and Validation Maturity Model), has been initiated by a consortium of industrial companies, consultancy & service agencies and an academic institute, operating and residing in the Netherlands. MB-V/sup 2/M/sup 2/ is designed to be universally applicable, to unite the strengths of known (verification and validation) improvement models and to reflect proven work practices. It recommends a metrics base to select process improvements and to track and control implementation of improvement actions. This paper outlines the model and addresses the current status.
Extracted statement — Domain Name: Software
Research field: Software Engineering
Title: Mapping human values and scrum roles: a study on students' preferences
Abstract: Despite the long tradition on the study of human values, the impact of this field in the software engineering domain is rarely studied. To these regards, this study focuses on applying human values to agile software development process, more specifically to scrum roles. Thus, the goal of the study is to explore possible associations between human values and scrum roles preferences among students. Questionnaires are designed by employing the Short Schwartz's Value Survey and are distributed among 57 students. The results of the quantitative analysis process consisting of descriptive statistics, linear regression models and Pearson correlation coefficients, revealed that values such as power and self-direction influence the preference for the product owner role, the value of hedonism influences the preference for scrum masters and self-direction is associated with team members' preference.
Extracted statement — Subjects: students
Research field: Software Engineering
Title: Crowdsourcing to elicit requirements for MyERP application
Abstract: Crowdsourcing is an emerging method to collect requirements for software systems. Applications seeking global acceptance need to meet the expectations of a wide range of users. Collecting requirements and arriving at consensus with a wide range of users is difficult using traditional method of requirements elicitation. This paper presents crowdsourcing based approach for German medium-size software company MyERP that might help the company to get access to requirements from non-German customers. We present the tasks involved in the proposed solution that would help the company meet the goal of eliciting requirements at a fast pace with non-German customers.
Extracted statement — Utilities in CrowdRE: Task
Research field: Software Engineering
Title: Mining Twitter Feeds for Software User Requirements
Abstract: Twitter enables large populations of end-users of software to publicly share their experiences and concerns about software systems in the form of micro-blogs. Such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems. However, automatically capturing, classifying, and presenting useful tweets is not a trivial task. Challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam. Motivated by these challenges, this paper reports on a three-fold study that is aimed at leveraging Twitter as a main source of software user requirements. The main objective is to enable a responsive, interactive, and adaptive data-driven requirements engineering process. Our analysis is conducted using 4,000 tweets collected from the Twitter feeds of 10 software systems sampled from a broad range of application domains. The results reveal that around 50% of collected tweets contain useful technical information. The results also show that text classifiers such as Support Vector Machines and Naive Bayes can be very effective in capturing and categorizing technically informative tweets. Additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software-relevant tweets.
Extracted statement — Utilities in CrowdRE: Task