Eric Ries was born in September 1978. He graduated from Yale University and moved to silicon Valley in the beginning of the millennium. He pioneered the philosophy of Lean Startup, based on his experience with multiple startups, primary being IMVU which he co-founded along with Will Harvey in 2004. Eric Ries originated his Lean Startup philosophy after getting inspired from the Japanese Philosophy of Lean Manufacturing.

The Lean Startup:How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges.

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A Survey of App Store Analysis for Software Engineering

Mobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better meeting user expectations. Thus, automated approaches have been proposed in literature with the aim of reducing the effort required for analyzing feedback contained in user reviews via automatic classification/prioritization according to specific topics. In this paper, we introduce SURF (Summarizer of User Reviews Feedback), a novel approach to condense the enormous amount of information that developers of popular apps have to manage due to user feedback received on a daily basis. SURF relies on a conceptual model for capturing user needs useful for developers performing maintenance and evolution tasks. Then it uses sophisticated summarisation techniques for summarizing thousands of reviews and generating an interactive, structured and condensed agenda of recommended software changes. We performed an end-to-end evaluation of SURF on user reviews of 17 mobile apps (5 of them developed by Sony Mobile), involving 23 developers and researchers in total. Results demonstrate high accuracy of SURF in summarizing reviews and the usefulness of the recommended changes. In evaluating our approach we found that SURF helps developers in better understanding user needs, substantially reducing the time required by developers compared to manually analyzing user (change) requests and planning future software changes.

What would users change in my app? summarizing app reviews for recommending software changes

Since ontology-driven software development (ODSD) is an integration of ontology technologies and model-driven software development (MDSD), it is necessary to identify and analyse technologies applied in MDSD. We define basic concepts, such as model-driven engineering, metamodelling, model transformation and technological space, and describe the state-of-the-art implementations of these concepts.

Model-Driven Software Development

Nowadays, users can easily submit feedback about software products in app stores, social media, or user groups. Moreover, software vendors are collecting massive amounts of implicit feedback in the form of usage data, error logs, and sensor data. These trends suggest a shift toward data-driven user-centered identification, prioritization, and management of software requirements. Developers should be able to adopt the requirements of masses of users when deciding what to develop and when to release. They could systematically use explicit and implicit user data in an aggregated form to support requirements decisions. The goal is data-driven requirements engineering by the masses and for the masses.

Toward Data-Driven Requirements Engineering

Context: Categorising software systems according to their functionality yields many benefits to both users and developers. Goal: 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.

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Clustering Mobile Apps Based on Mined Textual Features

Sentiment analysis is the process of identifying the polarity of sentiments held in opinions found in pieces of text and classifying them as positive, negative or neutral. In this paper, we propose the implementation of a sentiment analysis tool that is conducted over text found in Arabic new media including web forums, comments on newspaper articles and other websites with evaluative content. The expected input of the tool, which is informal Colloquial Arabic, is characterized to be of highly non-structured nature and subject to trends used to express sentiments. Our solution is a novel technique that merges the area of human computation with the task of natural language processing.

A proposed sentiment analysis tool for modern Arabic using human-based computing

We introduce a theoretical characterisation of feature lifecycles in app stores, to help app developers to identify trends and to find undiscovered requirements. To illustrate and motivate app feature lifecycle analysis, we use our theory to empirically analyse the migratory and non-migratory behaviours of 4,053 non-free features from two App Stores (Samsung and BlackBerry). The results reveal that, in both stores, intransitive features (those that neither migrate nor die out) exhibit significantly different behaviours with regard to important properties, such as their price. Further correlation analysis also highlights differences between trends relating price, rating, and popularity. Our results indicate that feature lifecycle analysis can yield insights that may also help developers to understand feature behaviours and attribute relationships.

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Feature lifecycles as they spread, migrate, remain, and die in App Stores

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.

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App Store Effects on Software Engineering Practices

We present the implementation and evaluation of a sentiment analysis system that is conducted over Arabic text with evaluative content. Our system is broken into two different components. The first component is a game that enables users to annotate large corpuses of text in a fun manner. The game produces necessary linguistic resources that will be used by the second component which is the sentimental analyzer. Two different algorithms have been designed to employ these linguistic resources to analyze text and classify it according to its sentimental polarity. The first approach is using sentimental tag patterns, which reached a precision level of 56.14%. The second approach is the sentimental majority approach which relies on calculating the number of negative and positive phrases in the sentence and classifying the sentence according to the dominant polarity. The results after evaluating the system for the first sentimental majority approach yielded the highest accuracy level reached by our system which is 60.5% while the second variation scored an accuracy of 60.32%.

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Afnan A. Al-Subaihin

Papers

Clustering Mobile Apps Based on Mined Textual Features

A proposed sentiment analysis tool for modern Arabic using human-based computing

Feature lifecycles as they spread, migrate, remain, and die in App Stores

App Store Effects on Software Engineering Practices

A System for Sentiment Analysis of Colloquial Arabic Using Human Computation