100 research outputs found
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
Owl Eyes: Spotting UI Display Issues via Visual Understanding
Graphical User Interface (GUI) provides a visual bridge between a software
application and end users, through which they can interact with each other.
With the development of technology and aesthetics, the visual effects of the
GUI are more and more attracting. However, such GUI complexity posts a great
challenge to the GUI implementation. According to our pilot study of
crowdtesting bug reports, display issues such as text overlap, blurred screen,
missing image always occur during GUI rendering on different devices due to the
software or hardware compatibility. They negatively influence the app
usability, resulting in poor user experience. To detect these issues, we
propose a novel approach, OwlEye, based on deep learning for modelling visual
information of the GUI screenshot. Therefore, OwlEye can detect GUIs with
display issues and also locate the detailed region of the issue in the given
GUI for guiding developers to fix the bug. We manually construct a large-scale
labelled dataset with 4,470 GUI screenshots with UI display issues and develop
a heuristics-based data augmentation method for boosting the performance of our
OwlEye. The evaluation demonstrates that our OwlEye can achieve 85% precision
and 84% recall in detecting UI display issues, and 90% accuracy in localizing
these issues. We also evaluate OwlEye with popular Android apps on Google Play
and F-droid, and successfully uncover 57 previously-undetected UI display
issues with 26 of them being confirmed or fixed so far.Comment: Accepted to 35th IEEE/ACM International Conference on Automated
Software Engineering (ASE 20
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
Data-Driven Decisions and Actions in Today’s Software Development
Today’s software development is all about data: data about the software product itself, about the process and its different stages, about the customers and markets, about the development, the testing, the integration, the deployment, or the runtime aspects in the cloud. We use static and dynamic data of various kinds and quantities to analyze market feedback, feature impact, code quality, architectural design alternatives, or effects of performance optimizations. Development environments are no longer limited to IDEs in a desktop application or the like but span the Internet using live programming environments such as Cloud9 or large-volume repositories such as BitBucket, GitHub, GitLab, or StackOverflow. Software development has become “live” in the cloud, be it the coding, the testing, or the experimentation with different product options on the Internet. The inherent complexity puts a further burden on developers, since they need to stay alert when constantly switching between tasks in different phases. Research has been analyzing the development process, its data and stakeholders, for decades and is working on various tools that can help developers in their daily tasks to improve the quality of their work and their productivity. In this chapter, we critically reflect on the challenges faced by developers in a typical release cycle, identify inherent problems of the individual phases, and present the current state of the research that can help overcome these issues
Reduce API Debugging Overhead via Knowledge Prepositioning
OpenAPI indicates a behavior where producers offer Application Programming
Interfaces (APIs) to help end-users access their data, resources, and services.
Generally, API has many parameters that need to be entered. However, it is
challenging for users to understand and document these parameters correctly.
This paper develops an API workbench to help users learn and debug APIs. Based
on this workbench, much exploratory work has been proposed to reduce the
overhead of learning and debugging APIs. We explore the knowledge, such as
parameter characteristics (e.g., enumerability) and constraints (e.g.,
maximum/minimum value), from the massive API call logs to narrow the range of
parameter values. Then, we propose a fine-grained approach to enrich the API
documentation by extracting dependency knowledge between APIs. Finally, we
present a learning-based prediction method to predict API execution results
before the API is called, significantly reducing user debugging cycles. The
experiments evaluated on the online system show that this work's approach
substantially improves the user experience of debugging OpenAPIs.Comment: arXiv admin note: text overlap with arXiv:1509.01626,
arXiv:1502.01710 by other author
On Using Blockchains for Safety-Critical Systems
Innovation in the world of today is mainly driven by software. Companies need
to continuously rejuvenate their product portfolios with new features to stay
ahead of their competitors. For example, recent trends explore the application
of blockchains to domains other than finance. This paper analyzes the
state-of-the-art for safety-critical systems as found in modern vehicles like
self-driving cars, smart energy systems, and home automation focusing on
specific challenges where key ideas behind blockchains might be applicable.
Next, potential benefits unlocked by applying such ideas are presented and
discussed for the respective usage scenario. Finally, a research agenda is
outlined to summarize remaining challenges for successfully applying
blockchains to safety-critical cyber-physical systems
An NLP-based tool for software artifacts analysis
Software developers rely on various repositories and communication channels to exchange relevant information about their ongoing tasks and the status of overall project progress. In this context, semi-structured and unstructured software artifacts have been leveraged by researchers to build recommender systems aimed at supporting developers in different tasks, such as transforming user feedback in maintenance and evolution tasks, suggesting experts, or generating software documentation. More specifically, Natural Language (NL) parsing techniques have been successfully leveraged to automatically identify (or extract) the relevant information embedded in unstructured software artifacts. However, such techniques require the manual identification of patterns to be used for classification purposes. To reduce such a manual effort, we propose an NL parsingbased tool for software artifacts analysis named NEON that can automate the mining of such rules, minimizing the manual effort of developers and researchers. Through a small study involving human subjects with NL processing and parsing expertise, we assess the performance of NEON in identifying rules useful to classify app reviews for software maintenance purposes. Our results show that more than one-third of the rules inferred by NEON are relevant for the proposed task. Demo webpage: https://github.com/adisorbo/NEON too
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
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