Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments

Jeong, Jongwook; Kim, Neunghoe

doi:10.1007/s10515-021-00295-w

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…The context in which the problems of indicator management are analyzed change over time, and the approach related to the definition and prioritization of indicators has remained inert, given that it has not adapted to the changes that have arisen in the context of problems reported by customers [8,22]. The consequences of this negligence can lead to delays in the schedule and additional costs; a high level of defects in the solution delivered; and most importantly, the delivery of a solution that does not fully satisfy the user's needs [3].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The systematic literature review carried out by Inayat et al [25], found seventeen requirement-related engineering practices adopted by professionals, including five challenges of traditional requirements engineering overcomes with the adoption of agile requirements engineering and eight challenges related to agile requirements engineering. Some examples can be mentioned, in critical projects, of failures in solutions or in projects in which the deficiency in prioritizing requirements indicators caused problems in the final delivery of the solution [3].…”

Section: Requirement Indicatorsmentioning

confidence: 99%

“…Only with improvements in the steps that make up management processes, did researchers begin to realize the importance of obtaining a clear understanding of business needs [2]. Some frameworks or methodologies, such as Ikons Customer Relationship Management (iCRM) and tool support for consistency management and validation of requirements (MaramaAIC), prescribe and/or suggest a set of steps to be followed to efficiently and effectively achieve a clear definition of business needs [2][3][4][5]. However, they adopt a more traditional structure, where business needs are assessed based on specific techniques for defining and prioritizing indicators [6].…”

Section: Introductionmentioning

confidence: 99%

“…If any interested party disagrees with the result, it is necessary to understand the reasons for which the disagreements occurred, and if applicable, review the model in question and perform the previous steps again. 3.…”

mentioning

confidence: 99%

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A Model for the Definition, Prioritization and Optimization of Indicators

et al. 2022

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Context: The definition and prioritization of indicators is now a common reality and an integral part of the evolution of the strategic, technical and business processes of any organization, whether public or private. Purpose: This paper proposes a new model regarding the definition and prioritization of indicators. Herein, we also investigate the definition and prioritization models currently adopted by academia and industry, and analyze the context of the proposed strategies against the traditional view of indicator definition currently adopted. In addition, we conducted a survey with organizations that had well-defined indicator management processes, seeking to identify customer expectations with a new indicator management model proposed by this work. Method: To gather evidence, we defined a methodology that relates the literature review and an exploratory case study with the application of an experiment. Driven by a set of research questions, this methodology comprised four main phases: planning, literature review, experiment execution and documentation of results. The method used is supported by some techniques, such as design thinking, design sprint and the Cynefin framework. Results: The analysis of the results was carried out in two different ways: Through the verification of the achievement of specific objectives and through a questionnaire applied to assess the degrees of perception of all employees who participated in the work. Regarding the specific objectives, it is clear that most of the objectives were achieved. Regarding the applied questionnaire, it is clear that, although the collaborators did not have adequate knowledge regarding the conceptual and practical aspects of some approaches used in the proposed model, there was a general perception that the model, in fact, supported top management for decision making. For professionals, the proposed model has a restricted scope; that is, it does not serve all types of organizations. Conclusion: The model proposed in this work proved to be effective, considering that the indicators were defined, prioritized and optimized, with a focus on the user experience. As future work, we intend to expand the scope of the model’s performance, evaluating business indicators alongside IT indicators.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Requirement Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Model for the Definition, Prioritization and Optimization of Indicators

et al. 2022

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“…In the software development lifecycle, requirement gathering and managing are very important. Requirements can be defined as stakeholders' expectations from the system end product, functional abilities, or non-functionalities [7]. Requirement engineering is considered at an early stage in the software product development life cycle.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Recent Requirements Gathering and Management Tools in Requirements Engineering for IoT-Enabled Sustainable Cities

2022

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The Internet of Things (IoT) is a paradigm that facilitates the proliferation of different devices such as sensors and Radio Frequency Identification (RFIDs) for real-time applications such as healthcare and sustainable cities. The growing popularity of IoT opens up new possibilities, and one of the most notable applications is related to the evolving sustainable city paradigm. A sustainable city is normally designed in such a way to consider the environmental impact and a social, economic, and resilient habitat for existing populations without compromising the ability of future generations to experience the same, while the process of managing project requirements is known as requirements management. To design a high-quality project, effective requirements management is imperative. A number of techniques are already available to perform the requirement gathering process, and software developers apply them to collect the requirements. Nevertheless, they are facing many issues in gathering requirements due to a lack of literature on the selection of appropriate methods, which affects the quality of the software. The software design quality can be improved by using requirements capture and management techniques. Some tools are used to comprehend the system accurately. In this paper, a qualitative comparison of requirements-gathering tools using Artificial Intelligence (AI) and requirements-management tools is presented for sustainable cities. With all the tools and techniques available for capturing and managing requirements, it has been proven that software developers have a wide range of alternatives for selecting the best tool that fits their needs, such as chosen by the AI agent. This effort will aid in the development of requirements for IoT-enabled sustainable cities.

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Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Khan,

Ullah,

Khan

et al. 2023

Concurrency and Computation

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SummaryEnd‐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.

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Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments

Cited by 7 publications

References 23 publications

A Model for the Definition, Prioritization and Optimization of Indicators

A Model for the Definition, Prioritization and Optimization of Indicators

A Comparison of Recent Requirements Gathering and Management Tools in Requirements Engineering for IoT-Enabled Sustainable Cities

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

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