2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)最新文献_第7页

Quantifying the Performance Impact of SQL Antipatterns on Mobile Applications 量化SQL反模式对移动应用程序的性能影响

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00015

Yingjun Lyu, Ali S. Alotaibi, William G. J. Halfond

引用次数: 7

Ticket Tagger: Machine Learning Driven Issue Classification 票据标注器:机器学习驱动的问题分类

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00070

Rafael Kallis, Andrea Di Sorbo, G. Canfora, Sebastiano Panichella

{"title":"Ticket Tagger: Machine Learning Driven Issue Classification","authors":"Rafael Kallis, Andrea Di Sorbo, G. Canfora, Sebastiano Panichella","doi":"10.1109/ICSME.2019.00070","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00070","url":null,"abstract":"Software maintenance is crucial for software projects evolution and success: code should be kept up-to-date and error-free, this with little effort and continuous updates for the end-users. In this context, issue trackers are essential tools for creating, managing and addressing the several (often hundreds of) issues that occur in software systems. A critical aspect for handling and prioritizing issues involves the assignment of labels to them (e.g., for projects hosted on GitHub), in order to determine the type (e.g., bug report, feature request and so on) of each specific issue. Although this labeling process has a positive impact on the effectiveness of issue processing, the current labeling mechanism is scarcely used on GitHub. In this demo, we introduce a tool, called Ticket Tagger, which leverages machine learning strategies on issue titles and descriptions for automatically labeling GitHub issues. Ticket Tagger automatically predicts the labels to assign to issues, with the aim of stimulating the use of labeling mechanisms in software projects, this to facilitate the issue management and prioritization processes. Along with the presentation of the tool's architecture and usage, we also evaluate its effectiveness in performing the issue labeling/classification process, which is critical to help maintainers to keep control of their workloads by focusing on the most critical issue tickets.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117197847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

An Approach to Recommendation of Verbosity Log Levels Based on Logging Intention 基于日志意图的详细日志级别推荐方法

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00022

H. Anu, Jie Chen, Wenchang Shi, Jianwei Hou, Bin Liang, Bo Qin

{"title":"An Approach to Recommendation of Verbosity Log Levels Based on Logging Intention","authors":"H. Anu, Jie Chen, Wenchang Shi, Jianwei Hou, Bin Liang, Bo Qin","doi":"10.1109/ICSME.2019.00022","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00022","url":null,"abstract":"Verbosity levels of logs are designed to discriminate highly diverse runtime events, which facilitates system failure identification through simple keyword search (e.g., fatal, error). Verbosity levels should be properly assigned to logging statements, as inappropriate verbosity levels would confuse users and cause a lot of redundant maintenance effort. However, to achieve such a goal is not an easy task due to the lack of practical specifications and guidelines towards verbosity log level usages. The existing research has built a classification model on log related quantitative metrics such as log density to improve logging level practice. Though such quantitative metrics can reveal logging characteristics, their contributions on logging level decision are limited, since valuable logging intention information buried in logging code context can not be captured. In this paper, we propose an automatic approach to help developers determine the appropriate verbosity log levels. More specially, our approach discriminates different verbosity log level usages based on code context features that contain underlying logging intention. To validate our approach, we implement a prototype tool, VerbosityLevelDirector, and perform a case study to measure its effectiveness on four well-known open source software projects. Evaluation results show that VerbosityLevelDirector achieves high performance on verbosity level discrimination and outperforms the baseline approaches on all those projects. Furthermore, through applying noise handling technique, our approach can detect previously unknown inappropriate verbosity level configurations in the code repository. We have reported 21 representative logging level errors with modification advice to issue tracking platforms of the examined software projects and received positive feedback from their developers. The above results confirm that our work can help developers make a better logging level decision in real-world engineering.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134387595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study 研究不稳定的建筑气味演变:一个探索性的案例研究

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00090

Darius Sas, P. Avgeriou, F. Fontana

引用次数: 14

What Do Developers Discuss about Biometric APIs? 开发者对生物识别api讨论了什么?

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00053

Zhe Jin, K. Chee, Xin Xia

{"title":"What Do Developers Discuss about Biometric APIs?","authors":"Zhe Jin, K. Chee, Xin Xia","doi":"10.1109/ICSME.2019.00053","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00053","url":null,"abstract":"With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Design Smell Detection and Analysis for Open Source Java Software 开源Java软件的气味检测与分析设计

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00104

A. Imran

引用次数: 6

Linguistic Change in Open Source Software 开源软件中的语言变化

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00045

Miroslav Tushev, Saket Khatiwada, Anas Mahmoud

{"title":"Linguistic Change in Open Source Software","authors":"Miroslav Tushev, Saket Khatiwada, Anas Mahmoud","doi":"10.1109/ICSME.2019.00045","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00045","url":null,"abstract":"In this paper, we seek to advance the state-of-the-art in code evolution analysis research and practice by statistically analyzing, interpreting, and formally describing the evolution of code lexicon in Open Source Software (OSS). The underlying hypothesis is that, similar to natural language, code lexicon falls under the remit of evolutionary principles. Therefore, adapting theories and statistical models of natural language evolution to code is expected to provide unique insights into software evolution. Our analysis in this paper is conducted using 2,000 OSS systems sampled from a broad range of application domains. Our results show that a) OSS projects exhibit a significant shift in their linguistic identity over time, b) different syntactic structures of code lexicon evolve differently, c) different factors of OSS development and different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic software maintenance and program comprehension activities, and gain new theoretical insights into the complex interplay between linguistic change and various system and human aspects of OSS development.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115568174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Inappropriate Usage Examples in Web API Documentations Web API文档中的不当用法示例

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00052

Masaki Hosono, Susumu Tokumoto, S. Monpratarnchai, H. Washizaki, Kiyoshi Honda, Hiromasa Nagumo, Hisanobu Sonoda, Y. Fukazawa, Kazuki Munakata, Takao Nakagawa, Yusuke Nemoto

引用次数: 1

Personalized Code Recommendation 个性化代码推荐

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00047

Tam The Nguyen, P. Vu, T. Nguyen

{"title":"Personalized Code Recommendation","authors":"Tam The Nguyen, P. Vu, T. Nguyen","doi":"10.1109/ICSME.2019.00047","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00047","url":null,"abstract":"The current state-of-the-art methods in code recommendation mostly take the crowd-based approach. The basic idea is to collect and extract code patterns from a large pool of available source code and use those code patterns for recommendations. However, different programmers have different coding styles, levels of experience, and knowledge about libraries and frameworks, all of which cause different uses of variable names, classes, and methods. When code of different programmers is combined, such differences are blurred, which could hurt the performance of the code recommendation tool for a specific programmer. In the paper, we explore a new research direction in code recommendation which focuses on personal coding patterns of programmers. As a proof of concept, we have developed a personalized code recommendation model for suggesting variable declaration and initialization code. Our techniques learn personalized code patterns for each programmer based on their coding history. The preliminary evaluation shows that our recommendation model is highly effective. For example, when evaluating on a programmer, our approach has top-1 accuracy of 62% and top-3 accuracy of 70% on recommending declaration types. Our approach has top-1 and top-3 accuracy of 67% and 76%, respectively, on recommending initialization method sequences. Furthermore, our model also outperforms the baselines significantly in these experiments.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127820607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery 少数据跟踪:基于分类的跟踪链接恢复的主动学习

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00020

Chris Mills, Javier Escobar-Avila, Aditya R. Bhattacharya, Grigoriy Kondyukov, Shayok Chakraborty, S. Haiduc

{"title":"Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery","authors":"Chris Mills, Javier Escobar-Avila, Aditya R. Bhattacharya, Grigoriy Kondyukov, Shayok Chakraborty, S. Haiduc","doi":"10.1109/ICSME.2019.00020","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00020","url":null,"abstract":"Previous work has established both the importance and difficulty of establishing and maintaining adequate software traceability. While it has been shown to support essential maintenance and evolution tasks, recovering traceability links between related software artifacts is a time consuming and error prone task. As such, substantial research has been done to reduce this barrier to adoption by at least partially automating traceability link recovery. In particular, recent work has shown that supervised machine learning can be effectively used for automating traceability link recovery, as long as there is sufficient data (i.e., labeled traceability links) to train a classification model. Unfortunately, the amount of data required by these techniques is a serious limitation, given that most software systems rarely have traceability information to begin with. In this paper we address this limitation of previous work and propose an approach based on active learning, which substantially reduces the amount of training data needed by supervised classification approaches for traceability link recovery while maintaining similar performance.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133205360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13