User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

EvLog: Evolving Log Analyzer for Anomalous Logs Identification

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving log events, and unstable log sequences. In this paper, we propose a novel unsupervised approach named Evolving Log analyzer (EvLog) to mitigate these challenges. We first build a multi-level representation extractor to process logs without parsing to prevent errors from the parser. The multi-level representations preserve the essential semantics of logs while leaving out insignificant changes in evolving events. EvLog then implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively, which outperforms other state-of-the-art approaches by a wide margin. To our best knowledge, this is the first study on tackling anomalous logs over software evolution. We believe our work sheds new light on the impact of software evolution with the corresponding solutions for the log analysis community

An empirical investigation of relevant changes and automation needs in modern code review

SUMMARY of the PAPER: This paper investigates the approaches and tools that, from a "developer's point of view", are still needed to facilitate Modern Code Review (MCR) activities. To that end, we empirically elicited a taxonomy of recurrent review change types that characterize MCR. This by (i) qualitatively and quantitatively analyzing review changes/commits of ten open-source projects; (ii) integrating MCR change types from existing taxonomies available from the literature; and (iii) surveying 52 developers to integrate eventually missing change types in the taxonomy. The results of our study highlight that the availability of new emerging development technologies (e.g., cloud-based technologies) and practices (e.g., continuous delivery) has pushed developers to perform additional activities during MCR and that additional types of feedback are expected by reviewers. Our participants provided also recommendations, specified techniques to employ, and highlighted the data to analyze for building recommender systems able to automate the code review activities composing our taxonomy. In summary, this study sheds some more light on the approaches and tools that are still needed to facilitate MCR activities, confirming the feasibility and usefulness of using summarization techniques during MCR activities. We believe that the results of our work represent an essential step for meeting the expectations of developers and supporting the vision of full or partial automation in MCR. REPLICATION PACKAGE: https://zenodo.org/record/3679402#.XxgSgy17Hxg PREPRINT: https://spanichella.github.io/img/EMSE-MCR-2020.pdfRecent research has shown that available tools for Modern Code Review (MCR) are still far from meeting the current expectations of developers. The objective of this paper is to investigate the approaches and tools that, from a developer's point of view, are still needed to facilitate MCR activities. To that end, we first empirically elicited a taxonomy of recurrent review change types that characterize MCR. The taxonomy was designed by performing three steps: (i) we generated an initial version of the taxonomy by qualitatively and quantitatively analyzing 211 review changes/commits and 648 review comments of ten open-source projects; then (ii) we integrated into this initial taxonomy, topics, and MCR change types of an existing taxonomy available from the literature; finally, (iii) we surveyed 52 developers to integrate eventually missing change types in the taxonomy. Results of our study highlight that the availability of new emerging development technologies (e.g., cloud-based technologies) and practices (e.g., continuous delivery) has pushed developers to perform additional activities during MCR and that additional types of feedback are expected by reviewers. Our participants provided recommendations, specified techniques to employ, and highlighted the data to analyze for building recommender systems able to automate the code review activities composing our taxonomy. We surveyed 14 additional participants (12 developers and 2 researchers), not involved in the previous survey, to qualitatively assess the relevance and completeness of the identified MCR change types as well as assess how critical and feasible to implement are some of the identified techniques to support MCR activities. Thus, with a study involving 21 additional developers, we qualitatively assess the feasibility and usefulness of leveraging natural language feedback (automation considered critical/feasible to implement) in supporting developers during MCR activities. In summary, this study sheds some more light on the approaches and tools that are still needed to facilitate MCR activities, confirming the feasibility and usefulness of using summarization techniques during MCR activities. We believe that the results of our work represent an essential step for meeting the expectations of developers and supporting the vision of full or partial automation in MC

A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward