As program comprehension is a vast research area, it is necessary to get an overview of its rising and falling trends. We performed an n-gram frequency analysis on titles, abstracts and keywords of 1885 articles about program comprehension from the years 2000–2014. According to this analysis, the most rising trends are feature location and open source systems, the most falling ones are program slicing and legacy systems.
A similar trend analysis @cite_9 was performed on the papers from nine Mining Software Repositories conferences. The authors analyzed another, albeit related, research field. Our study is methodologically inspired by this paper in some aspects, particularly the idea of n-gram frequency analysis. However, our approach differs in many ways. For example, the authors analyzed only nine years of one particular conference, while we performed a web search for the keywords. On the other hand, they analyzed full texts, while we only processed the metadata. An n-gram query language syntax was inspired by Google Ngram Viewer @cite_4 . We also considered directly using this tool for our study since it offers a text corpus many orders of magnitude larger than ours. While the time period is broader, too, it does not include the last two years. Furthermore, it is not specialized to computer science and the corpus consists of books, which are less suitable for latest trend analysis than research articles. It is also possible to analyze Q &A (question and answer) sites such as Stack Overflow to find out trending topics. In @cite_8 , a technique called latent Ditrichlet allocation @cite_7 was used to recover topics from natural language texts.
@cite_9 @cite_4 @cite_7 @cite_8
2049158239 2019096529 1880262756 2056894403
On the occasion of the 10th anniversary of the MSR conference, it is a worthwhile exercise to meditate on the past, present and future of our research discipline. Indeed, since the MSR community has experienced a big influx of researchers bringing in new ideas, state-of-the art technology and contemporary research methods it is unclear what the future might bring. In this paper, we report on a text mining exercise applied on the complete corpus of MSR papers to reflect on where we come from; where we are now; and where we should be going. We address issues like the trendy (and outdated) research topics; the frequently (and less frequently) cited cases; the popular (and emerging) mining infrastructure; and finally the proclaimed actionable information which we are deemed to uncover. We constructed a corpus of digitized texts containing about 4 of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities. We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. Programming question and answer (QA questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL.