How PHP Releases Are Adopted in the Wild?

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

This empirical paper examines the adoption of PHP releases in the the contemporary world wide web. Motivated by continuous software engineering practices and software traceability improvements for release engineering, the empirical analysis is based on big data collected by web crawling. According to the empirical results based on discrete time-homogeneous Markov chain (DTMC) analysis, (i)~adoption of PHP releases has been relatively uniform across the domains observed, (ii) which tend to also adopt either old or new PHP releases relatively infrequently. Although there are outliers, (iii) downgrading of PHP releases is generally rare. To some extent, (iv) the results vary between the recent history from 2016 to early 2017 and the long-run evolution in the 2010s. In addition to these empirical results, the paper contributes to the software evolution and release engineering research traditions by elaborating the applied use of DTMCs for systematic empirical tracing of online software deployments.

Related collections

Most cited references 20

Record: found
Abstract: found
Article: found

Is Open Access

Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

Philipp Singer, Denis Helic, Behnam Taraghi … (2014)

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.