Does Involving Users in Software Development Really Influence System Success?

Abelein, Ulrike; Sharp, Helen; Paech, Barbara

doi:10.1109/ms.2013.124

Cited by 43 publications

(23 citation statements)

References 10 publications

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“…The end users were included in this study because they are the main reason why an SDP is conducted and will be the users of the end product [40]. They are also essential to the success of an SDP since they need to provide what needs to be done as part of the project [41]. The top ten soft skills that were identified by end users are listed in Table VII.…”

Section: Q2: Soft Skills That Facilitate the Delivery Of Successful Smentioning

confidence: 99%

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Soft Skills for Software Project Team Members

Mtsweni¹,

Hörne²,

Poll³

2016

IJCTE

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Abstract-Software development usually occurs in the context of software project management (SPM). An often cited challenge in the area of software project management is the high failure rate of software development projects (SPDs). One of the factors contributing to the failure of SDPs is the lack of soft issues, also known as soft skills or human aspects. The main purpose of this research was to identify and categorise the soft skills that facilitate the delivery of successful SDPs. To this end a survey was conducted in the form of a questionnaire as the research instrument. The results of the survey underwent a quantitative analysis and it was found that a variety of soft skills are important in facilitating the delivery of successful SDPs. Although a wide range of soft skills were identified, not all soft skills can be considered to be of crucial or equal importance. Index Terms-Soft skills, software development projects, software project failure.

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Section: Q2: Soft Skills That Facilitate the Delivery Of Successful Smentioning

confidence: 99%

“…The three common soft skills conform to the characteristics of SDPs. SDPs are conducted in the form of teams, and the scope of the project is guided by the end user or the primary customer [41].…”

Section: Q2: Soft Skills That Facilitate the Delivery Of Successful Smentioning

confidence: 99%

Soft Skills for Software Project Team Members

Mtsweni¹,

Hörne²,

Poll³

2016

IJCTE

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“…The main problem for developers is that existing app distribution platforms provide limited support to systematically filter, classify, and aggregate user feedback to derive requirements [14]. Moreover, manually reading each user review to gather the useful ones is not feasible considering that popular apps (e.g., Facebook) receive hundreds of reviews every day [5], [15].…”

Section: Introductionmentioning

confidence: 99%

Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews

Palomba

Salza

Ciurumelea

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

133

139

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Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44,683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%)

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“…User reviews are recognized as a valuable source to improve APPs and increase the value for users [9] [18], as the reviews help developers to better understand user needs as a type of collective knowledge [19]. However, existing APP platforms provide limited support for developers to systematically filter, aggregate, and classify user feedback to derive requirements [9].…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Requirements from App User Reviews

Hui

Liang

2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Review function, as a feedback mechanism from users to developers and vendors, is provided by most APP distribution platforms that allow users to rate and comment an APP after using it. User reviews are recognized as a valuable source to improve APPs and increase the value for users. With the sharp increase in the amount of user reviews, how to effectively and efficiently analyze the user reviews and identify potential and critical user needs from them to improve the APPs becomes a challenge. In this paper, we propose an approach to automatically identify requirements information and further classify them into functional and non-functional requirements from user reviews, using a combination of information retrieval technique (TF-IDF) and NLP technique (regular expression) with human intervention in keywords selection for requirements identification and classification. We validated the proposed approach with the user reviews collected from a popular APP iBooks in English App Store, and further investigated the cost and return of our approach: how the size of sample reviews for keywords selection (cost) affects the classification results in precision, recall, and F-measure (return). The results show that when setting an appropriate size of sample reviews, our approach receives a relatively stable precision, recall, and F-measure of requirements classification, in particular for non-functional requirements, which is meaningful and practical for APP developers to elicit requirements from user reviews.

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Does Involving Users in Software Development Really Influence System Success?

Cited by 43 publications

References 10 publications

Soft Skills for Software Project Team Members

Soft Skills for Software Project Team Members

Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews

Identification and Classification of Requirements from App User Reviews

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