In this paper, we study the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. Through developer interviews and questionnaires, we uncover findings that highlight and quantify the effects of three high-level app store themes: bridging the gap between developers and users, increasing market transparency and affecting mobile release management. Our findings have implications for testing, requirements engineering and mining software repositories research fields. These findings can help guide future research in supporting mobile app developers through a deeper understanding of the app store-developer interaction.
Categorising software systems according to their functionality yields many benefits to both users and developers. Objective: In order to uncover the latent clustering of mobile apps in app stores, we propose a novel technique that measures app similarity based on claimed behaviour. Method: Features are extracted using information retrieval augmented with ontological analysis and used as attributes to characterise apps. These attributes are then used to cluster the apps using agglomerative hierarchical clustering. We empirically evaluate our approach on 17,877 apps mined from the BlackBerry and Google app stores in 2014. Results: The results show that our approach dramatically improves the existing categorisation quality for both Blackberry (from 0.02 to 0.41 on average) and Google (from 0.03 to 0.21 on average) stores. We also find a strong Spearman rank correlation (ρ = 0.96 for Google and ρ = 0.99 for BlackBerry) between the number of apps and the ideal granularity within each category, indicating that ideal granularity increases with category size, as expected. Conclusions: Current categorisation in the app stores studied do not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method. 'App Task Manager Pro'), or a set of apps that belong in the same suite (e.g.
Recent software maintenance models have included impact analysis and accounting for ripple effect as one of their stages. This paper describes and explains the reformulation of Yau and Collofello's ripple‐effect algorithm and its validity within the software‐maintenance process. Completely automatic computation of ripple effect has until now proved troublesome; we show how our approximation algorithm helps to overcome this. Our Ripple Effect and Stability Tool (REST) which uses our approximated algorithm to compute ripple effect for C programs, is described. Eleven C programs are used in an initial investigation into whether our approximated algorithm can replace Yau and Collofello's original algorithm for the purpose of automatic computation of ripple effect. The Pearson correlation coefficient for the two versions of the algorithm across the eleven programs shows a high correlation. Copyright © 2001 John Wiley & Sons, Ltd.
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In this paper, we describe the preliminary results of a pilot survey conducted to collect information on social media use in global software systems development. We created an on-line survey for developers who are using social media to communicate at work and whose work falls within the domain of software systems development, including web applications. Our results show that social media can enable better communication through the software system development process. 91% of respondents said that using social media has improved their working life.
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