Alessandro Murgia

Do developers feel emotions? an exploratory analysis of emotions in software artifacts

Software development is a collaborative activity in which developers interact to create and maintain a complex software system. Human collaboration inevitably evokes emotions like joy or sadness, which can affect the collaboration either positively or negatively, yet not much is known about the individual emotions and their role for software development stakeholders. In this study, we analyze whether development artifacts like issue reports carry any emotional information about software development. This is a first step towards verifying the feasibility of an automatic tool for emotion mining in software development artifacts: if humans cannot determine any emotion from a software artifact, neither can a tool. Analysis of the Apache Software Foundation issue tracking system shows that developers do express emotions (in particular gratitude, joy and sadness). However, the more context is provided about an issue report, the more human raters start to doubt and nuance their interpretation of emotions. More investigation is needed before building a fully automatic emotion mining tool.

On the Distribution of Bugs in the Eclipse System

The distribution of bugs in software systems has been shown to satisfy the Pareto principle, and typically shows a power-law tail when analyzed as a rank-frequency plot. In a recent paper, Zhang showed that the Weibull cumulative distribution is a very good fit for the Alberg diagram of bugs built with experimental data. In this paper, we further discuss the subject from a statistical perspective, using as case studies five versions of Eclipse, to show how log-normal, Double-Pareto, and Yule-Simon distributions may fit the bug distribution at least as well as the Weibull distribution. In particular, we show how some of these alternative distributions provide both a superior fit to empirical data and a theoretical motivation to be used for modeling the bug generation process. While our results have been obtained on Eclipse, we believe that these models, in particular the Yule-Simon one, can generalize to other software systems.

Assessing traditional and new metrics for object-oriented systems

We present an extensive analysis of software metrics for 111 object-oriented systems written in Java. For each system, we considered 18 traditional metrics such as LOC and Chidamber and Kemerer metrics, as well as metrics derived from complex network theory and social network analysis. These metrics were computed at class level. We also considered two metrics at system level, namely the total number of classes and interfaces, and the fractal dimension. We discuss the distribution of these metrics, and their correlation, both at class and at system level. We found that most metrics follow a leptokurtotic distribution. Only a couple of metrics have patent normal behavior while three others are very irregular, and even bimodal. The statistics gathered allow us to study and discuss the variability of metrics along different systems, and to devise a roadmap for further research.

The emotional side of software developers in JIRA

ABSTRACTIssue tracking systems store valuable data for testing hy-potheses concerning maintenance, building statistical pre-diction models and (recently) investigating developer affec-tiveness. For the latter, issue tracking systems can be minedto explore developers emotions, sentiments and politeness, affects for short. However, research on affect detection insoftware artefacts is still in its early stage due to the lack ofmanually validated data and tools.In this paper, we contribute to the research of affectson software artefacts by providing a labeling of emotionspresent on issue comments.We manually labeled 2,000 issue comments and 4,000 sen-tences written by developers with emotions such as love,joy, surprise, anger, sadness and fear. Labeled commentsand sentences are linked to software artefacts reported inour previously published dataset (containing more than 1Kprojects, more than 700K issue reports and more than 2million issue comments). The enriched dataset presented inthis paper allows the investigation of the role of affects insoftware development.

An empirical study of social networks metrics in object-oriented software

We study the application to object-oriented software of new metrics, derived from Social Network Analysis. Social Networks metrics, as for instance, the EGO metrics, allow to identify the role of each single node in the information flow through the network, being related to software modules and their dependencies. These metrics are compared with other traditional software metrics, like the Chidamber-Kemerer suite, and software graph metrics. We examine the empirical distributions of all the metrics, bugs included, across the software modules of several releases of two large Java systems, Eclipse and Netbeans. We provide analytical distribution functions suitable for describing and studying the observed distributions. We study also correlations among metrics and bugs. We found that the empirical distributions systematically show fat-tails for all the metrics. Moreover, the various metric distributions look very similar and consistent across all system releases and are also very similar in both the studied systems. These features appear to be typical properties of these software metrics.

Happy Birthday! A trend analysis on past MSR papers

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.

On the influence of maintenance activity types on the issue resolution time

The ISO/IEC 14764 standard specifies four types of software maintenance activities spanning the different motivations that software engineers have while performing changes to an existing software system. Undoubtedly, this classification has helped in organizing the workflow within software projects, however for planning purposes the relative time differences for the respective tasks remains largely unexplored. In this empirical study, we investigate the influence of the maintenance type on issue resolution time. From GitHubs issue repository, we analyze more than 14000 issue reports taken from 34 open source projects and classify them as corrective, adaptive, perfective or preventive maintenance. Based on this data, we show that the issue resolution time depends on the maintenance type. Moreover, we propose a statistical model to describe the distribution of the issue resolution time for each type of maintenance activity. Finally, we demonstrate the usefulness of this model for scheduling the maintenance workload.

Fine-tuning spectrum based fault localisation with frequent method item sets

Continuous integration is a best practice adopted in modern software development teams to identify potential faults immediately upon project build. Once a fault is detected it must be repaired immediately, hence continuous integration provides an ideal testbed for experimenting with the state of the art in fault localisation. In this paper we propose a variant of what is known as spectrum based fault localisation, which leverages patterns of method calls by means of frequent itemset mining. We compare our variant (we refer to it as patterned spectrum analysis) against the state of the art and demonstrate on 351 real faults drawn from five representative open source java projects that patterned spectrum analysis is more effective in localising the fault.

Among the Machines: Human-Bot Interaction on Social Q&A Websites

With the rise of social media and advancements in AI technology, human-bot interaction will soon be commonplace. In this paper we explore human-bot interaction in STACK OVERFLOW, a question and answer website for developers. For this purpose, we built a bot emulating an ordinary user answering questions concerning the resolution of git error messages. In a first run this bot impersonated a human, while in a second run the same bot revealed its machine identity. Despite being functionally identical, the two bot variants elicited quite different reactions.

An exploratory qualitative and quantitative analysis of emotions in issue report comments of open source systems

Software development—just like any other human collaboration—inevitably evokes emotions like joy or sadness, which are known to affect the group dynamics within a team. Today, little is known about those individual emotions and whether they can be discerned at all in the development artifacts produced during a project. This paper analyzes (a) whether issue reports—a common development artifact, rich in content—convey emotional information and (b) whether humans agree on the presence of these emotions. From the analysis of the issue comments of 117 projects of the Apache Software Foundation, we find that developers express emotions (in particular gratitude, joy and sadness). However, the more context is provided about an issue report, the more human raters start to doubt and nuance their interpretation. Based on these results, we demonstrate the feasibility of a machine learning classifier for identifying issue comments containing gratitude, joy and sadness. Such a classifier, using emotion-driving words and technical terms, obtains a good precision and recall for identifying the emotion love, while for joy and sadness a lower recall is obtained.