Bridging the Vendor-User Gap in Enterprise Cloud Software Development through Data-Driven Requirements Engineering

The shift from on-premise to cloud software has fundamentally changed the interactions between enterprise software vendors and their users. Where user involvement has traditionally been a challenge, increasingly large amounts of user input now allow for data-driven requirements engineering (RE). Research has paid little attention so far to the changes entailed by data-driven RE and addressed neither technical nor empirical perspectives of data-driven RE in enterprise software development. We aim to understand how the increasing availability of large amounts of user input impact RE in enterprise cloud software development. We provide a conceptualization of the newly available user input and how it changes traditional RE. We collect and analyze rich data from multiple product units at a leading enterprise software company and examine the integration of user input into RE; specifically requirements discovery, prioritization, experimentation, and specification. We thereby aim to contribute to non-normative and empirical work on RE

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

Feedback Gathering from an Industrial Point of View

Feedback communication channels allow end-users to express their needs, which can be considered in software development and evolution. Although feedback gathering and analysis have been identified as an important topic and several researchers have started their investigation, information is scarce on how software companies currently elicit end-user feedback. In this study, we explore the experiences of software companies with respect to feedback gathering. The results of a case study and online survey indicate two sides of the same coin: on the one hand, most software companies are aware of the relevance of end-user feedback for software evolution and provide feedback channels, which allow end-users to communicate their needs and problems. On the other hand, the quantity and quality of the feedback received varies. We conclude that software companies still do not fully exploit the potential of end-user feedback for software development and evolution

Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review

© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented

CrowdSurfer: Seamlessly Integrating Crowd-Feedback Tasks nto Everyday Internet Surfing

Crowd feedback overcomes scalability issues of feedback collection on interactive website designs. However, collecting feedback on crowdsourcing platforms decouples the feedback provider from the context of use. This creates more effort for crowdworkers to immerse into such context in crowdsourcing tasks. In this paper, we present CrowdSurfer, a browser extension that seamlessly integrates design feedback collection in crowdworkers’ everyday internet surfing. This enables the scalable collection of in situ feedback and, in parallel, allows crowdworkers to flexibly integrate their work into their daily activities. In a field study, we compare the CrowdSurfer against traditional feedback collection. Our qualitative and quantitative results reveal that, while in situ feedback with the CrowdSurfer is not necessarily better, crowdworkers appreciate the effortless, enjoyable, and innovative method to conduct feedback tasks. We contribute with our findings on in situ feedback collection and provide recommendations for the integration of crowdworking tasks in everyday internet surfing

GARUSO: a gamification approach for involving stakeholders outside organizational reach in requirements engineering

Stakeholder participation is a key success factor of Requirements Engineering (RE). Typically, the techniques used for identifying and involving stakeholders in RE assume that stakeholders can be identified among the members of the organizations involved when a software system is ordered, developed or maintained—and that these stakeholders can be told or even mandated to contribute. However, these assumptions no longer hold for many of today’s software systems where significant stakeholders (in particular, end-users and people affected by a system) are outside organizational reach: They are neither known nor can they easily be identified in the involved organizations nor can they be told to participate in RE activities. We have developed the GARUSO approach to address this problem. It uses a strategy for identifying stakeholders outside organizational reach and a social media platform that applies gamification for motivating these stakeholders to participate in RE activities. In this article, we describe the GARUSO approach and report on its empirical evaluation. We found that the identification strategy attracted a crowd of stakeholders outside organizational reach to the GARUSO platform and motivated them to participate voluntarily in collaborative RE activities. From our findings, we derived a first set of design principles on how to involve stakeholders outside organizational reach in RE. Our work expands the body of knowledge on crowd RE regarding stakeholders outside organizational reach

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering

Adaptive software-based Feedback Acquisition: A Persona-based design

Users’ feedback is vital to improve software quality and it provides developers with a rich knowledge on how software meets users’ requirements in practice. Feedback informs how software should adapt, or be adapted, at runtime and what evolutionary actions to take in the next release. However, studies have noted that accommodating the different preferences of users on how feedback should be requested is a complex task and requires a careful engineering process. This calls for an adaptive feedback acquisition mechanisms to cater for such variability. In this paper, we tackle this problem by employing the concept of Persona to aid software engineers understand the various users’ behaviours and improve their ability to design feedback acquisition techniques more efficiently. We create a set of personas based on a mixture of qualitative and quantitative studies and propose PAFA, a Persona-based method for Adaptive Feedback Acquisition