The advent of online software distribution channels like Apple Inc.'s App Store and Google Inc.'s Google Play has offered developers a single, low cost, and powerful distribution mechanism. These online stores help users discover apps as well as leave a review. Ratings and reviews add value to both the developer and potential new users by providing a crowd-sourced indicator of app quality. Hence, for developers it is important to get positive reviews and high ratings to ensure that an app has a viable future. But, what exactly do users say on these app stores? And more importantly, what is the experience that compels a user to leave either a positive or a negative rating? Our analysis of 8.7 million reviews from 17,330 apps shows that users tend to leave short, yet informative reviews, and the rating as well as the category influences the length of a review. In this preliminary study, we found that users will leave longer messages when they rate an app poorly, and the depth of feedback in certain categories is significantly higher than for other

Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics - for example, in terms of ldquoaveragerdquo values - can be highly misleading. Many metrics, it turns out, are distributed like wealth - with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.

Usability defects can be found either by formal usability evaluation methods or indirectly during system testing or usage. No matter how they are discovered, these defects must be tracked and reported. However, empirical studies indicate that usability defects are often not clearly and fully described. This study aims to identify the state of the art in reporting of usability defects in the software engineering and usability engineering literature. We conducted a systematic literature review of usability defect reporting drawing from both the usability and software engineering literature from January 2000 until March 2016. As a result, a total of 57 studies were identified, in which we classified the studies into three categories: reporting usability defect information, analysing usability defect data and key challenges. Out of these, 20 were software engineering studies and 37 were usability studies. The results of this systematic literature review show that usability defect reporting processes suffer from a number of limitations, including: mixed data, inconsistency of terms and values of usability defect data, and insufficient attributes to classify usability defects. We make a number of recommendations to improve usability defect reporting and management in software engineering.

Online software distribution channels such as Apple Inc.'s App Store and Google Inc.'s Google Play provide a platform for third-party app distribution. These online stores feature a public review system, allowing users to express opinions regarding purchased apps. These reviews can influence product-purchasing decisions via polarised sentiment (1 to 5 stars) and user expressed opinion. For developers, reviews are a user-facing crowd-sourced indicator of app quality. Hence, high ratings and positive reviews affect the viability of an app's commercial feasibility. However, it is less clear what information is contained within these reviews, and more importantly, if an analysis of these reviews can inform developers of design priorities as opposed to just influencing purchasing decisions. We analysed 8.7 million reviews from 17,330 apps on the App Store and found that the most frequently used words in user reviews lean toward expressions of sentiment despite employment of only approximately 37% of the words within the English language dictionary. Furthermore, the range of words used to express negative opinions is significantly higher than when positive sentiments are expressed.

This paper presents the capturing and recording of reasoning behind software architecture design to encourage architects to more carefully consider design decisions and better support future maintenance.