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- Afzal, Wasif, et al.
(författare)
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On using grey literature and google scholar in systematic literature reviews in software engineering
- 2020
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Ingår i: IEEE Access. - United States. - 2169-3536. ; 8, s. 36226-36243
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Tidskriftsartikel (refereegranskat)abstract
- © 2013 IEEE. Context: The inclusion of grey literature (GL) is important to remove publication bias while gathering available evidence regarding a certain topic. The number of systematic literature reviews (SLRs) in Software Engineering (SE) is increasing but we do not know about the extent of GL usage in these SLRs. Moreover, Google Scholar is rapidly becoming a search engine of choice for many researchers but the extent to which it can find the primary studies is not known. Objective: This tertiary study is an attempt to i) measure the usage of GL in SLRs in SE. Furthermore this study proposes strategies for categorizing GL and a quality checklist to use for GL in future SLRs; ii) explore if it is feasible to use only Google Scholar for finding scholarly articles for academic research. Method: We have conducted a systematic mapping study to measure the extent of GL usage in SE SLRs as well as to measure the feasibility of finding primary studies using Google Scholar. Results and conclusions: a) Grey Literature: 76.09% SLRs (105 out of 138) in SE have included one or more GL studies as primary studies. Among total primary studies across all SLRs (6307), 582 are classified as GL, making the frequency of GL citing as 9.23%. The intensity of GL use indicate that each SLR contains 5 primary studies on average (total intensity of GL use being 5.54). The ranking of GL tells us that conference papers are the most used form 43.3% followed by technical reports 28.52%. Universities, research institutes, labs and scientific societies together make up 67.7% of GL used, indicating that these are useful sources for searching GL. We additionally propose strategies for categorizing GL and criteria for evaluating GL quality, which can become a basis for more detailed guidelines for including GL in future SLRs. b) Google Scholar Results: The results show that Google Scholar was able to retrieve 96% of primary studies of these SLRs. Most of the primary studies that were not found using Google Scholar were from grey sources.
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| 2. |
- Fatima, Rubia, et al.
(författare)
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Improving Software Requirements Reasoning by Novices : A story-based approach
- 2019
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Ingår i: IET Software. - : Institution of Engineering and Technology (IET). - 1751-8806 .- 1751-8814. ; 13:6, s. 564-574
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Tidskriftsartikel (refereegranskat)abstract
- Context: Requirements-elicitation is one of the essential steps towards software design and construction. Business analysts and stakeholders often face challenges in gathering or conveying key software requirements. There are many methods, and tools designed by researchers and practitioners, but with the invention of new technologies, there appears to be a need to make requirements gathering and design-rationale process more efficient. Storytelling is an emerging concept and researchers are witnessing its effectiveness in education, community-building, information system, and requirement elicitation. Objective: Objectives of this study are to i) devise a method for requirements elicitation and improving design-rationales using story-based technique; ii) evaluate the effectiveness of the aforementioned proposed activity. Methodology: To answer the research objectives, we have i) conducted open-ended interviews to get feedback on our proposed method; ii) case requirement from a running project to map how this method can be useful; and iii) performed empirical evaluation of the proposed card-based activity. Result: i) Our regression model has shown that participants' perception regarding the ease of use and the fun in the game has an ultimate effect on requirements elicitation through enhancing user's desire to play the game, hence, increasing the collaborative learning outcomes of the game; ii) Our results have shown that using team-based activities helps the less-experienced designers to argue through design rationales and better elicit software requirements. Our results have reinforced the finding that using game-based solutions not only enhances communication and develops trust between stakeholders but also helps in motivating participants of requirements activity; iii) Initial results (from interview and empirical evaluation) for the proposed technique and method show positive results. Improvement in the process and activity as suggested by the participants will be accommodated in future studies.
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| 3. |
- Fatima, Rubia, et al.
(författare)
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Retrieving arXiv, SocArXiv, and SSRN metadata for initial review screening
- 2023
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Ingår i: Information and Software Technology. - : ELSEVIER. - 0950-5849 .- 1873-6025. ; 161
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Forskningsöversikt (refereegranskat)abstract
- Context: Researchers around the globe invest a lot of time searching the literature for performing reviews (Systematic Literature Review (SLR), Multivocal Literature Review (MLR)). The steps to performing the review includes inclusion of the grey literature, preprints, and quality assessed non-peer reviewed literature (the purpose is to minimize the publication bias). The initial screening of the papers takes time and bibliographic information is only available online for the researcher(s). Objective: Objective of our study is to propose, design, and develop a method that will help the research community to download the basic information of the papers (title, abstract, author) for the searched query from arxiv, SSRN, and SocArxiv (Social Science ArXiv). Method: We used Web scraping to extract data from the servers and save it in excel file. To retrieve the desired query from the databases, a Python code is used. Two methods have been discussed in the study to download the metadata of the searched query. Results: We have used different queries (such as "grey literature", "testing software", and "python" etc.) to see the results of our proposed method. Furthermore, we cross-verified the results with the online search results of the databases. Conclusion: Initial results from the preliminary pilot evaluations show that it is a viable method to search, download, and shortlist the research articles information (title, abstract etc.) from arXiv,1 SSRN,2 and SocArXiv.3 For external validity more evaluations are needed.
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| 4. |
- Yasin, Affan, et al.
(författare)
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Python Data Odyssey : Mining User feedback from Google Play store
- 2024
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Ingår i: Data in Brief. - : Elsevier. - 2352-3409. ; 54
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Tidskriftsartikel (refereegranskat)abstract
- ContextThe Google Play Store is widely recognized as one of the largest platforms for downloading applications, both free and paid1. On a daily basis, millions of users avail themselves of this marketplace, sharing their thoughts through various means such as star ratings, user comments, suggestions, and feedback. These insights, in the form of comments and feedback, constitute a valuable resource for organizations, competitors, and emerging companies seeking to expand their market presence. These comments provide insights into app deficiencies, suggestions for new features, identified issues, and potential enhancements. Unlocking the potential of this repository of suggestions holds significant value.ObjectiveThis study sought to gather and analyze user reviews from the Google Play store for leading game apps. The primary aim was to construct a dataset for subsequent analysis utilizing requirements engineering, machine learning, and competitive assessment.MethodologyThe authors employed a Python-based web scraping method to extract a comprehensive set of over 429,000+ reviews from the Google Play pages of selected apps. The scraped data encompassed reviewer names (removed due to privacy), ratings, and the textual content of the reviews.ResultsThe outcome was a dataset comprising the extracted user reviews, ratings, and associated metadata. A total of 429,000+ reviews were acquired through the scraping process for popular apps like Subway Surfers, Candy Crush Saga, PUBG Mobile, among others. This dataset not only serves as a valuable educational resource for instructors, aiding in the training of students in data analysis, but also offers practitioners the opportunity for in-depth examination and insights (in the past data of top apps).
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