Exploring the intersection between software industry and Software Engineering education - A systematic mapping of Software Engineering Trends

Social Media Prediction (SMP) is an emerging powerful tool attracting the attention of researchers and practitioners alike. Despite its many merits, SMP has also several weaknesses, as it is limited by data issues, like bias and noise, and the lack of confident predictions and generalizable results. The goal of this paper is to survey popular and trending fields of SMP from 2015 and onwards and discuss the predictive models used. We elaborate on results found in the literature, while categorizing the forecasting attempts, based on specific values (source of data, algorithm used, outcome of prediction etc.). Finally, we present our findings and conduct statistical analysis on our dataset and critique the outcome of the attempted prediction reported by the reviewed papers. Our research indicates that results are ambiguous, as not all forecasting models can predict with high accuracy, and prediction seems dependable on the associated field, although some of the documented attempts are promising. More than half (53.1%) of the examined attempts achieved a valid prediction, nearly one fifth (18.8%) did not, while the remaining 28.1% is characterized as plausible or partially validated. By reviewing recent and up-to-date literature and by providing statistics, this paper provides SMP researchers with a guide on methods, algorithms, techniques, prediction success and challenges on three main categories that aid SMP exploration.

Social media prediction: a literature review

Pioneering educators discuss how they inject realism into global-software-engineering education.

How Best to Teach Global Software Engineering? Educators Are Divided

This paper presents work on using data extracted from Twitter to predict the outcome of the latest USA presidential elections on 8th of November 2016 in three key states: Florida, Ohio and N. Carolina, focusing on the two dominant candidates: Donald J. Trump and Hillary Clinton. Our method comprises two steps: pre-processing and analysis and it succeeded in capturing negative and positive sentiment towards these candidates, and predicted the winner in these States, who eventually won the presidency, when other similar attempts in the literature have failed. We discuss the strengths and weaknesses of our method proposing directions for further work.

A Method for Predicting the Winner of the USA Presidential Elections using Data extracted from Twitter

Global software engineering education (GSEE) is aimed at providing software engineering (SE) students with knowledge, skills, and understanding of working in globally distributed arrangements so they can be prepared for the global SE (GSE) paradigm. It is important to understand the challenges involved in GSEE for improving the quality and experience of educators and students. This paper reports the findings of an empirical study on the socio-cultural aspects of GSEE. A case study was conducted involving 14 participants from ten different universities in eight countries. The data was analyzed using grounded theory’s open coding procedure. The key contributions of this paper are the identification and description of seven dimensions of socio-cultural distance that caused several significant challenges in the courses: 1) language differences; 2) concept of time; 3) attitude toward grades; 4) assumptions about national culture; 5) differences in autonomy; 6) influence of the course lecturer; and 7) work habits. Recommendations from this paper that are expected to benefit GSEE educators and students include: cross-cultural orientation of students prior to the course; use of various strategies to support better comprehension of different English accents (e.g., speaking slowly, replaying recorded video messages, and text chatting); and educators familiarizing themselves and their students with the relevant GSE and GSEE literature.

Socio-Cultural Challenges in Global Software Engineering Education

We describe an experience in teaching global software engineering (GSE) using distributed Scrum augmented with industrial best practices. Our unique instructional technique had students work in both same-site and cross-site teams to contrast the two modes of working. The course was a collaboration between Aalto University, Finland and University of Victoria, Canada. Fifteen Canadian and eight Finnish students worked on a single large project, divided into four teams, working on interdependent user stories as negotiated with the industrial product owner located in Finland. Half way through the course, we changed the teams so each student worked in both a local and a distributed team. We studied student learning using a mixed-method approach including 14 post-course interviews, pre-course and Sprint questionnaires, observations, meeting recordings, and repository data from git and Flow dock, the primary communication tool. Our results show no significant differences between working in distributed vs. Non-distributed teams, suggesting that Scrum helps alleviate many GSE problems. Our post-course interviews and survey data allows us to explain this effect, we found that students over time learned to better self-select tasks with less inter-team dependencies, to communicate more, and to work better in teams.

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Learning global agile software engineering using same-site and cross-site teams

Traditional media has always played a large role in elections by informing voters and shaping opinions, and recently, social media and various Internet information sources have also become considerable influencers on the voters. There is data publicly available on how these information sources and media channels are being used, which could potentially be analyzed for their effects on the election process. This chapter aims to determine if social media, Internet traffic, and traditional media data can be used to predict elections by searching for patterns between the data and poll numbers for 2016 US Republican and Democratic primaries. The results suggest that machine learning models with linear regression can produce quite accurate predictions; also statistically significant correlations were found between polls and betting odds and polls and Facebook page likes. More sophisticated methods could allow for better forecasting using this publicly available data.

Predicting 2016 US Presidential Election Polls with Online and Media Variables

Do politicians’ answers to voting advice applications reflect their sincere beliefs? Comparing publicly and confidentially stated ideological positions in a candidate-centred electoral context

Choosing a candidate can be difficult in open-list proportional representation systems, with many candidates on display. Under such circumstances, candidates' personal vote-earning attributes (PVEAs) become useful for voters since PVEAs function as information shortcuts (heuristics) that reduce the cognitive costs of casting a ballot. However, recent research has demonstrated that more cognitively demanding information, namely candidates' ideological positions, also matters for candidates' electoral success. Yet, much is unknown about the circumstances under which ideology comes into play. In this study, we demonstrate that the availability of easily applied heuristics (operationalized via the level of recognizable candidates on party-lists) conditions the effect of candidates' ideological positioning on intra-party success. Our analyses show that ideology matters most when the share of recognizable candidates with typical personal vote-earning attributes on the list is limited. The effect of ideological distance from the party gradually disappears as the share of recognizable candidates on the list increases. The results suggest that when the supply of candidates with personal vote-earning attributes is limited, voters use ideological cues as the base for their voting decisions. This article is protected by copyright. All rights reserved 

Veikko Isotalo

Papers

Learning global agile software engineering using same-site and cross-site teams

Predicting 2016 US Presidential Election Polls with Online and Media Variables

Do politicians’ answers to voting advice applications reflect their sincere beliefs? Comparing publicly and confidentially stated ideological positions in a candidate-centred electoral context

When does ideology matter? Party‐lists, personal attributes and the effect of ideology on intra‐party success