Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00015
Yingjun Lyu, Ali S. Alotaibi, William G. J. Halfond
In mobile applications, local databases have become an important component, providing mobile users with a responsive and secure service for data access and management. However, using local databases comes with a cost. Studies have shown that they are one of the most resource consuming components on mobile devices. Improper usage of the local database can even severely impact the responsiveness of an application. In this paper, we conducted a literature review and a benchmark study to investigate problematic programming practices with respect to database usage. Our results present a comprehensive overview of the current knowledge about these practices, and introduce new knowledge about the impact of these practices on the resource consumption of mobile applications.
{"title":"Quantifying the Performance Impact of SQL Antipatterns on Mobile Applications","authors":"Yingjun Lyu, Ali S. Alotaibi, William G. J. Halfond","doi":"10.1109/ICSME.2019.00015","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00015","url":null,"abstract":"In mobile applications, local databases have become an important component, providing mobile users with a responsive and secure service for data access and management. However, using local databases comes with a cost. Studies have shown that they are one of the most resource consuming components on mobile devices. Improper usage of the local database can even severely impact the responsiveness of an application. In this paper, we conducted a literature review and a benchmark study to investigate problematic programming practices with respect to database usage. Our results present a comprehensive overview of the current knowledge about these practices, and introduce new knowledge about the impact of these practices on the resource consumption of mobile applications.","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":"117114642","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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00070
Rafael Kallis, Andrea Di Sorbo, G. Canfora, Sebastiano Panichella
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.
{"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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00022
H. Anu, Jie Chen, Wenchang Shi, Jianwei Hou, Bin Liang, Bo Qin
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.
{"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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00090
Darius Sas, P. Avgeriou, F. Fontana
Architectural smells may substantially increase maintenance effort and thus require extra attention for potential refactoring. While we currently understand this concept and have identified different types of such smells, we have not yet studied their evolution in depth. This is necessary to inform their prioritisation and refactoring. This study analyses the evolution of individual architectural smell instances over time, and the characteristics that define these instances. Three different types of architectural smells are taken into consideration and mined from a total of 524 versions across 14 different projects. The results show how different smell types differ in multiple aspects, such as their growth rate, the importance of the affected elements over time in the dependency network of the system, and the time each instance affects the system. They also cast valuable insights on what aspects are the most important to consider during prioritisation and refactoring activities.
{"title":"Investigating Instability Architectural Smells Evolution: An Exploratory Case Study","authors":"Darius Sas, P. Avgeriou, F. Fontana","doi":"10.1109/ICSME.2019.00090","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00090","url":null,"abstract":"Architectural smells may substantially increase maintenance effort and thus require extra attention for potential refactoring. While we currently understand this concept and have identified different types of such smells, we have not yet studied their evolution in depth. This is necessary to inform their prioritisation and refactoring. This study analyses the evolution of individual architectural smell instances over time, and the characteristics that define these instances. Three different types of architectural smells are taken into consideration and mined from a total of 524 versions across 14 different projects. The results show how different smell types differ in multiple aspects, such as their growth rate, the importance of the affected elements over time in the dependency network of the system, and the time each instance affects the system. They also cast valuable insights on what aspects are the most important to consider during prioritisation and refactoring activities.","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":"133142258","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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00053
Zhe Jin, K. Chee, Xin Xia
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.
{"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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00104
A. Imran
Software design smells have gained significant importance in recent years since those directly lead to the increase of design debts and drastically affect software quality. Although the impact of design smells is manifold, techniques to detect design smells using both rule based and data mining approaches have been explored to a limited extent. This research aims to provide a tool which uses software metrics as a guide to detect smells and also deploys Spectral Clustering to mine the software repositories and group similar smells. The tool has been partially implemented till now and initial experiments on 2,59,509 Lines of Code (LoC) covering 3,306 classes of real life open source Java software show 2,220 occurrences of four types of design smells.
{"title":"Design Smell Detection and Analysis for Open Source Java Software","authors":"A. Imran","doi":"10.1109/ICSME.2019.00104","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00104","url":null,"abstract":"Software design smells have gained significant importance in recent years since those directly lead to the increase of design debts and drastically affect software quality. Although the impact of design smells is manifold, techniques to detect design smells using both rule based and data mining approaches have been explored to a limited extent. This research aims to provide a tool which uses software metrics as a guide to detect smells and also deploys Spectral Clustering to mine the software repositories and group similar smells. The tool has been partially implemented till now and initial experiments on 2,59,509 Lines of Code (LoC) covering 3,306 classes of real life open source Java software show 2,220 occurrences of four types of design smells.","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":"125104492","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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00045
Miroslav Tushev, Saket Khatiwada, Anas Mahmoud
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.
{"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}
Pub Date : 2019-09-01DOI: 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
Application Programming Interfaces (APIs) are common in software development to reuse other products. Although the documentation allows API consumers to learn about API usages, it can be unreliable. Here, we investigate the characteristics of inappropriate usage examples in web API documentation by extracting and comparing OpenAPI Specifications from usage example-response pairs. About 65.5% of the endpoints have some form of inappropriate usage examples. Furthermore, mismatches are classified into four categories: undocumented keys pattern, dynamic keys pattern, unreturned keys pattern, and type mismatched pattern. Our results suggest that the number of keys in the response is correlated with the number of mismatches. These findings should assist both API providers and consumers who deal with unreliable documentation in web APIs.
{"title":"Inappropriate Usage Examples in Web API Documentations","authors":"Masaki Hosono, Susumu Tokumoto, S. Monpratarnchai, H. Washizaki, Kiyoshi Honda, Hiromasa Nagumo, Hisanobu Sonoda, Y. Fukazawa, Kazuki Munakata, Takao Nakagawa, Yusuke Nemoto","doi":"10.1109/ICSME.2019.00052","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00052","url":null,"abstract":"Application Programming Interfaces (APIs) are common in software development to reuse other products. Although the documentation allows API consumers to learn about API usages, it can be unreliable. Here, we investigate the characteristics of inappropriate usage examples in web API documentation by extracting and comparing OpenAPI Specifications from usage example-response pairs. About 65.5% of the endpoints have some form of inappropriate usage examples. Furthermore, mismatches are classified into four categories: undocumented keys pattern, dynamic keys pattern, unreturned keys pattern, and type mismatched pattern. Our results suggest that the number of keys in the response is correlated with the number of mismatches. These findings should assist both API providers and consumers who deal with unreliable documentation in web APIs.","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":"122331061","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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00047
Tam The Nguyen, P. Vu, T. Nguyen
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.
{"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}
Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00020
Chris Mills, Javier Escobar-Avila, Aditya R. Bhattacharya, Grigoriy Kondyukov, Shayok Chakraborty, S. Haiduc
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.
{"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}
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