On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used

Looking Over the Research Literature on Software Engineering from 2016 to 2018

This paper carries out a bibliometric analysis to detect (i) what is the most influential research on software engineering at the moment, (ii) where is being published that relevant research, (iii) what are the most commonly researched topics, (iv) and where is being undertaken that research (i.e., in which countries and institutions). For that, 6,365 software engineering articles, published from 2016 to 2018 on a variety of conferences and journals, are examined.This work has been funded by the Spanish Ministry of Science, Innovation, and Universities under Project DPI2016-77677-P, the Community of Madrid under Grant RoboCity2030-DIH-CM P2018/NMT-4331, and grant TIN2016-75850-R from the FEDER funds

A Gold Standard for Emotion Annotation in Stack Overflow

Software developers experience and share a wide range of emotions throughout a rich ecosystem of communication channels. A recent trend that has emerged in empirical software engineering studies is leveraging sentiment analysis of developers' communication traces. We release a dataset of 4,800 questions, answers, and comments from Stack Overflow, manually annotated for emotions. Our dataset contributes to the building of a shared corpus of annotated resources to support research on emotion awareness in software development.Comment: To appear in Proceedings of the 15th International Conference on Mining Software Repositories (MSR '18) Data Showcase Track, 28-29 May, Gothenburg, Swede

Optimizing the Failure Prediction in Deep Learning

  Avatars are computer-generated digital representations that people may use in the Predicting issues with software systems built from modules is the focus of this research. This data collection was used as a reference in order to accomplish this objective. The evaluation framework for reusable software components is provided by this research. The dataset of factors that play a role in the decision-making process has been run through the PSO algorithm. The primary objective is to provide a clever and time-saving method of choosing components. After filtering for ideal values, the dataset is utilized to train a deep learning model. Accuracy measurements including recall value, precision, and F1 score will be used to evaluate the effectiveness of the optimized component selection model. This research is significant because it provides a high-performance and accurate solution to a major problem in predicting. We have done our best to estimate the number of lines of code, the complexity, the design complexity, the projected time, the difficulty, the intelligence, and the efforts required. A model for discovering mistakes has been developed after the dataset was filtered to account for the ideal value. By keeping just the most crucial characteristics and getting rid of all optimized data, we have made the model more trustworthy. &nbsp

Аналіз ризиків проекту за допомогою текстового інтелектуального аналізу даних коментарів у системі управління проектами JIRA

During the study, a methodology was developed, and a software product was developed for project risk assessment based on developer communications, as well as the results of the program work on the data of the real project CASSANDRA of Apache Software Foundation. The methodology is implemented based on already well-known algorithms for determining the emotional components in the text of the VAD and matrix methods for project risk analysis using their developments that allow combining these different approaches. Obtaining the names of potential risks is performed using the model of constructing the LDA themes. The results allow us to determine the importance of the task by the communications and rank them in the middle of the project by the importance and need for additional attention that will allow project managers to understand and solve problems more quickly in the context of the product

How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow

Context: The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others' questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and feedback. Understanding the dynamics of the participation in Q&A communities is essential to improve the value of crowdsourced knowledge. Objective: In this paper, we investigate how information seekers can increase the chance of eliciting a successful answer to their questions on Stack Overflow by focusing on the following actionable factors: affect, presentation quality, and time. Method: We develop a conceptual framework of factors potentially influencing the success of questions in Stack Overflow. We quantitatively analyze a set of over 87K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests. The information seeker reputation is included as a control factor. Furthermore, to understand the role played by affective states in the success of questions, we qualitatively analyze questions containing positive and negative emotions. Finally, a survey is conducted to understand how Stack Overflow users perceive the guideline suggestions for writing questions. Results: We found that regardless of user reputation, successful questions are short, contain code snippets, and do not abuse with uppercase characters. As regards affect, successful questions adopt a neutral emotional style. Conclusion: We provide evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help. As for the role of affect, we empirically confirmed community guidelines that suggest avoiding rudeness in question writing.Comment: Preprint, to appear in Information and Software Technolog