Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However how well these extracted emotion states reflect people’s real emotions has not been thoroughly investigated. In this paper, we report a multi-level, longitudinal empirical study with 82 individual members in 27 project teams. We collected their self-reported retrospective emotion states on a weekly basis during their year-long projects and also extract corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using text-based algorithms often do not well reflect the dynamics of self-reported retrospective emotions. Besides, the extracted emotions match self-reported retrospective emotions better at the team level. Our results also suggest that individual personalities and team’s emotion display norms significantly impact the match/mismatch. Our results should warn the research community about the limitations and challenges of applying text-based emotion recognition tools in SE research.

Yi Wang

Shenzhen University

Collaboration with other people is a major theme in the information-seeking process. However, most existing works that address the location of features during the maintenance or evolution of software do not support collaboration, or they are focused on code as the main software artifact. Hence, collaborative feature location in models has not enjoyed much attention to date. In this work, we address this concern by proposing an approach, CoFLiM, that enables the collaboration of several domain experts in order to locate the model fragment of a target feature. CoFLiM uses the feature descriptions of the domain experts and their self-rated confidence level to automatically reformulate the relevant feature descriptions in a single query. This query guides the evolutionary algorithm of our approach that finds the model fragment of the feature being located. We evaluate CoFLiM in a real-world case study from our industrial partner. We analyze the impact of CoFLiM in terms of recall, precision, and the F-measure. Moreover, we compare the reformulation of CoFLiM with four baselines. We also perform a statistical analysis to show that the impact of the results is significant. Our results show that collaboration pays off in the location of features in models. The results also show that the self-rated confidence level can be used to locate features in models. Finally, the results show that there are no significant improvements when more than three domain experts are involved, which is relevant in those industrial contexts where the availability of domain experts is scarce.

Link to Publication: https://link.springer.com/article/10.1007%2Fs10515-019-00251-9

Francisca Pérez

SVIT Research GroupUniversidad San Jorge

Spain

Jaime Font

San Jorge University, Spain

Lorena Arcega

San Jorge University

Carlos Cetina

San Jorge University, Spain

Developers often reuse code snippets from online forums, such as Stack Overflow, GitHub Gists to learn API usages of software frameworks or libraries. Those code snippets often have ambiguous undeclared external references. This makes it difficult to learn and use those APIs correctly. Reusing those code snippets to solve development tasks also requires resolving external references of those APIs. However, manually resolving fully qualified names (FQN) of API elements is a non-trivial task. In this paper, we propose a novel context-sensitive technique, COSTER, to resolve FQNs of API elements in those code snippets. The technique collects locally specific source code elements as well as globally related tokens as the context of FQNs, calculate association score, and build an occurrence likelihood dictionary. While inferring an API element, it collects the code context and ranks candidate FQNs from the dictionary by considering the association score of the tokens in the context, similarity between the context, and similarity between the API element. Evaluation with code examples collected from GitHub and Stack Overflow posts shows that our proposed technique improves precision and recall by 3-18% compared to existing state-of-the-art techniques. The proposed technique significantly reduces the training time compared to the StatType, a state-of-the-art technique, without sacrificing accuracy. Extensive analyses on results establish the facts of the robustness of the proposed technique.

C M Khaled Saifullah

Department of Computer Science, University of Saskatchewan

Canada

Muhammad Asaduzzaman

Postdoctoral Research Fellow, Software Analysis and Intelligence Lab, Queen's University, Canada

Chanchal K. Roy

University of Saskatchewan

Canada

Inferring program transformations from concrete program changes has many potential uses, such as applying systematic program edits, refactoring, and automated program repair. Existing work for inferring program transformations usually rely on statistical information over a potentially large set of program-change examples. However, in many practical scenarios we do not have such a large set of program-change examples.

In this paper, we address the challenge of inferring a program transformation from one single example. Our core insight is that “big code” can provide effective guide for the generalization of a concrete change into a program transformation, i.e., code elements appearing in many files are general and should not be abstracted away. We first propose a framework for transformation inference, where programs are represented as hypergraphs to enable fine-grained generalization of transformations. We then design a transformation inference approach, GENPAT, that infers a program transformation based on code context and statistics from a big code corpus.

We have evaluated GENPAT under two distinct application scenarios, systematic editing and program repair. The evaluation on systematic editing shows that GENPAT significantly outperforms a state-of-the-art approach, SYDIT, with up to 5.5x correctly transformed cases. The evaluation on program repair suggests that GENPAT has the potential to be integrated in advanced program repair tools—GENPAT successfully repaired 19 real-world bugs in the Defects4J benchmark by simply applying transformations inferred from existing patches, where 4 bugs have never been repaired by any existing technique. Overall, the evaluation results suggest that GENPAT is effective for transformation inference and can potentially be adopted for many different applications.

Link to Publication: https://github.com/xgdsmileboy/GenPat

Jiajun Jiang

Peking University

China

Luyao Ren

Peking University

China

Yingfei Xiong

Peking University

China

Lingming Zhang

The University of Texas at Dallas

Execution logs, which are generated by logging code, are widely used in modern software projects for tasks like monitoring, debugging, and remote issue resolution. Ineffective logging would cause confusion, lack of information during problem diagnosis, or even system crash. However, it is challenging to develop and maintain logging code, as it inter-mixes with the feature code. Furthermore, unlike feature code, it is very challenging to verify the correctness of logging code. Currently developers usually rely on their intuition when performing their logging activities. There are no well established logging guidelines in research and practice. In this paper, we intend to derive such guidelines through mining the historical logging code changes. In particular, we have extracted and studied the Logging-Code-Issue-Introducing (LCII) changes in six popular large-scale Java-based open source software systems. Preliminary studies on this dataset show that: (1) both co-changed and independently changed logging code changes can contain fixes to the LCII changes; (2) the complexity of fixes to LCII changes are similar to regular logging code updates; (3) it takes longer for developers to fix logging code issues than regular bugs; and (4) the state-of-the-art logging code issue detection tools can only detect a small fraction (3%) of the LCII changes. This highlights the urgent need for this area of research and the importance of such a dataset.

Link to Publication: https://www.eecs.yorku.ca/~chenfsd/resources/emse2019_chen.pdf

Boyuan Chen

York University

Zhen Ming (Jack) Jiang

York University