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Filippo Lanubile

Publications and source records attributed to Filippo Lanubile.

At least 37 records · Page 2Linked to original sources
arXiv2021

Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists

The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a workshop conducted in a consulting company to understand how this transition is perceived by practitioners. Starting from the need for making AI experiments reproducible, the main themes that emerged are related to the use of the Jupyter Notebook as the primary prototyping tool, and the lack of support for software engineering best practices as well as data science specific functionalities.

cs.SE↗
arXiv2021

Assessment of Off-the-Shelf SE-specific Sentiment Analysis Tools: An Extended Replication Study

Sentiment analysis methods have become popular for investigating human communication, including discussions related to software projects. Since general-purpose sentiment analysis tools do not fit well with the information exchanged by software developers, new tools, specific for software engineering (SE), have been developed. We investigate to what extent SE-specific tools for sentiment analysis mitigate the threats to conclusion validity of empirical studies in software engineering, highlighted by previous research. First, we replicate two studies addressing the role of sentiment in security discussions on GitHub and in question-writing on Stack Overflow. Then, we extend the previous studies by assessing to what extent the tools agree with each other and with the manual annotation on a gold standard of 600 documents. We find that different SE-specific sentiment analysis tools might lead to contradictory results at a fine-grain level, when used 'off-the-shelf'. Conversely, platform-specific tuning or retraining might be needed to take into account differences in platform conventions, jargon, or document lengths.

cs.SE↗
arXiv2020

A Case Study on Tool Support for Collaboration in Agile Development

We report on a longitudinal case study conducted at the Italian site of a large software company to further our understanding of how development and communication tools can be improved to better support agile practices and collaboration. After observing inconsistencies in the way communication tools (i.e., email, Skype, and Slack) were used, we first reinforced the use of Slack as the central hub for internal communication, while setting clear rules regarding tools usage. As a second main change, we refactored the Jira Scrum board into two separate boards, a detailed one for developers and a high-level one for managers, while also introducing automation rules and the integration with Slack. The first change revealed that the teams of developers used and appreciated Slack differently with the QA team being the most favorable and that the use of channels is hindered by automatic notifications from development tools (e.g., Jenkins). The findings from the second change show that 85\% of the interviewees reported perceived improvements in their workflow. Despite the limitations due to the single nature of the reported case, we highlight the importance for companies to reflect on how to properly set up their agile work environment to improve communication and facilitate collaboration.

cs.SE↗
arXiv2020

Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?

In this paper, we address the problem of using sentiment analysis tools 'off-the-shelf,' that is when a gold standard is not available for retraining. We evaluate the performance of four SE-specific tools in a cross-platform setting, i.e., on a test set collected from data sources different from the one used for training. We find that (i) the lexicon-based tools outperform the supervised approaches retrained in a cross-platform setting and (ii) retraining can be beneficial in within-platform settings in the presence of robust gold standard datasets, even using a minimal training set. Based on our empirical findings, we derive guidelines for reliable use of sentiment analysis tools in software engineering.

cs.SE↗
arXiv2019

A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors

Code comprehension has been recently investigated from physiological and cognitive perspectives through the use of medical imaging. Floyd et al (i.e., the original study) used fMRI to classify the type of comprehension tasks performed by developers and relate such results to their expertise. We replicate the original study using lightweight biometrics sensors which participants (28 undergrads in computer science) wore when performing comprehension tasks on source code and natural language prose. We developed machine learning models to automatically identify what kind of tasks developers are working on leveraging their brain-, heart-, and skin-related signals. The best improvement over the original study performance is achieved using solely the heart signal obtained through a single device (BAC 87% vs. 79.1%). Differently from the original study, we were not able to observe a correlation between the participants' expertise and the classifier performance (tau = 0.16, p = 0.31). Our findings show that lightweight biometric sensors can be used to accurately recognize comprehension tasks opening interesting scenarios for research and practice.

cs.SE↗
arXiv2019

An empirical assessment of best-answer prediction models in technical Q&A sites

Technical Q&A sites have become essential for software engineers as they constantly seek help from other experts to solve their work problems. Despite their success, many questions remain unresolved, sometimes because the asker does not acknowledge any helpful answer. In these cases, an information seeker can only browse all the answers within a question thread to assess their quality as potential solutions. We approach this time-consuming problem as a binary-classification task where a best-answer prediction model is built to identify the accepted answer among those within a resolved question thread, and the candidate solutions to those questions that have received answers but are still unresolved. In this paper, we report on a study aimed at assessing 26 best-answer prediction models in two steps. First, we study how models perform when predicting best answers in Stack Overflow, the most popular Q&A site for software engineers. Then, we assess performance in a cross-platform setting where the prediction models are trained on Stack Overflow and tested on other technical Q&A sites. Our findings show that the choice of the classifier and automated parameter tuning have a large impact on the prediction of the best answer. We also demonstrate that our approach to the best-answer prediction problem is generalizable across technical Q&A sites. Finally, we provide practical recommendations to Q&A platform designers to curate and preserve the crowdsourced knowledge shared through these sites.

cs.SE↗
arXiv2018

On Developers' Personality in Large-scale Distributed Projects: The Case of the Apache Ecosystem

Large-scale distributed projects are typically the results of collective efforts performed by multiple developers, each one having a different personality. The study of developers' personalities has the potential of explaining their' behavior in various contexts. For example, the propensity to trust others, a critical factor to the success of global software engineering - has been found to influence positively the result of code reviews in distributed projects. In this paper, we perform a quantitative analysis of developers' personality in open source software projects, intended as an extreme form of distributed projects in which no single organization controls the project. We mine ecosystem-level data from the code commits and email messages contributed by the developers working on the Apache Software Foundation (ASF) projects, as representative of large scale-distributed projects. We find that developers become over time more conscientious, agreeable, and neurotic. Moreover, personality traits do not vary with their role, membership, and extent of contribution to the projects. We also find evidence that more open and more agreeable developers are more likely to become project contributors.

cs.SE↗
arXiv2018

A Benchmark Study on Sentiment Analysis for Software Engineering Research

A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.

cs.SE↗
arXiv2018

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.

cs.SE↗
arXiv2018

Mining Communication Data in a Music Community: A Preliminary Analysis

Comments play an important role within online creative communities because they make it possible to foster the production and improvement of authors' artifacts. We investigate how comment-based communication help shape members' behavior within online creative communities. In this paper, we report the results of a preliminary study aimed at mining the communication network of a music community for collaborative songwriting, where users collaborate online by first uploading new songs and then by adding new tracks and providing feedback in forms of comments.

cs.SI↗
arXiv2018

EmoTxt: A Toolkit for Emotion Recognition from Text

We present EmoTxt, a toolkit for emotion recognition from text, trained and tested on a gold standard of about 9K question, answers, and comments from online interactions. We provide empirical evidence of the performance of EmoTxt. To the best of our knowledge, EmoTxt is the first open-source toolkit supporting both emotion recognition from text and training of custom emotion classification models.

cs.HC↗
arXiv2017

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.

cs.CY↗
arXiv2017

Collaboration Success Factors in an Online Music Community

Online communities have been able to develop large, open-source software (OSS) projects like Linux and Firefox throughout the successful collaborations carried out by their members over the Internet. However, online communities also involve creative arts domains such as animation, video games, and music. Despite their growing popularity, the factors that lead to successful collaborations in these communities are not entirely understood. In this paper, we present a study on creative collaboration in a music community where authors write songs together by 'overdubbing,' that is, by mixing a new track with an existing audio recording. We analyzed the relationship between song- and author-related measures and the likelihood of a song being overdubbed. We found that recent songs, as well as songs with many reactions, are more likely to be overdubbed; authors with a high status in the community and a recognizable identity write songs that the community tends to build upon.

cs.HC↗
arXiv2017

A Preliminary Analysis on the Effects of Propensity to Trust in Distributed Software Development

Establishing trust between developers working at distant sites facilitates team collaboration in distributed software development. While previous research has focused on how to build and spread trust in absence of direct, face-to-face communication, it has overlooked the effects of the propensity to trust, i.e., the trait of personality representing the individual disposition to perceive the others as trustworthy. In this study, we present a preliminary, quantitative analysis on how the propensity to trust affects the success of collaborations in a distributed project, where the success is represented by pull requests whose code changes and contributions are successfully merged into the project's repository.

cs.SE↗
arXiv2017

Establishing Personal Trust-based Connections in Distributed Teams

Trust is a factor that dramatically contributes to the success or failure of distributed software teams. We present a research model showing that social communication between distant developers enables the affective appraisal of trustworthiness even from a distance, thus increasing project performance. To overcome the limitations of self-reported data, typically questionnaires, we focus on software projects following a pull request-based development model and approximate the overall performance of a software project with the history of successful collaborations occurring between developers.

cs.SI↗
arXiv2017

Sentiment Polarity Detection for Software Development

The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines.

cs.SE↗
arXiv2017

Emotion Detection Using Noninvasive Low Cost Sensors

Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.

cs.HC↗