A context model for IDE-based recommendation systems

Gašparić, Marko; Murphy, Gail C.; Ricci, Francesco

doi:10.1016/j.jss.2016.09.012

Cited by 29 publications

(16 citation statements)

References 24 publications

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“…External validity is related to the correctness of the results generalisation to the industrial practice [34]. In our case, the student volunteers who installed the GUI show certain similarities, according to the command knowledge, with the experienced IDE users working in academia and industry, who participated in the case study reported by Gasparic et al [22]. Nevertheless, the setting in which the study took place was not similar to industrial settings, hence, the generalisability of the results is questionable.…”

Section: ) External Validitymentioning

confidence: 72%

“…The model consists of 13 factors that describe the situations in which developers interact with the IDE, including: development activities, characteristics of the source code under development, and state of the IDE instance, described with visible and active GUI elements. The inclusion of the model in CNTX algorithm [8], which is the main recommendation algorithm in CoRe, is supported by the model evaluation results, which show that developers execute different commands in different contexts [22].…”

Section: A Ide Command Recommender Systemsmentioning

confidence: 79%

“…The input data for CoRe ICRS consists of IDE command execution histories belonging to different users and contextual information according to the context model suggested by Gasparic et al [22]. The model consists of 13 factors that describe the situations in which developers interact with the IDE, including: development activities, characteristics of the source code under development, and state of the IDE instance, described with visible and active GUI elements.…”

Section: A Ide Command Recommender Systemsmentioning

confidence: 99%

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IDE Interaction Support With Command Recommender Systems

Gašparić

Ricci

2020

IEEE Access

Self Cite

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Software developers' knowledge of integrated development environment (IDE) directly impacts on their productivity. IDE command recommender systems aim at identifying and convincingly presenting to software developers functionality that can help them to accomplish their daily tasks, without overloading them with well known or useless information. Command recommendation requires the estimation of both the utility of commands and the acceptance of the user for new command recommendations. In this paper, we focus on how and when such recommendations should be presented. We performed a long-term user study and our results show that IDE command recommendation must be presented with adequate descriptions of the commands and good usage examples. It seems that a higher frequency of recommendation notifications could be useful, but it should not be too intrusive, especially while developers are focusing on more demanding tasks. To improve recommendation acceptance rate, researchers should also focus on context-aware algorithms and tailor command recommendation timing.

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Section: ) External Validitymentioning

confidence: 72%

Section: A Ide Command Recommender Systemsmentioning

confidence: 79%

Section: A Ide Command Recommender Systemsmentioning

confidence: 99%

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IDE Interaction Support With Command Recommender Systems

Gašparić

Ricci

2020

IEEE Access

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“…In recommendation systems, a context model is a set of contextual factors that characterize a situation [3]. For code and API recommendation, since the set of all programming onsite data is excessively large [4], it is very expensive to collect all these data and use them for code and API recommendation.…”

Section: Context Datamentioning

confidence: 99%

“…CoDis generates personalized recommendations for a developer by analyzing his or her command usage history, command discovery and co-occurrence within the entire community, and the elapsed time between the developer's last activity and the time of recommendation. Gasparic et al statistically evaluated the correlations between IDE command usage and different situations and discovered that the contextual factors included in the model statistically correlated with the usage of IDE commands [3]. Moreover, they took into account the contexts in which a developer worked, and different commands were usually executed.…”

Section: Related Workmentioning

confidence: 99%

A Context Model for Code and API Recommendation Systems based on Programming Onsite Data

Zhang¹,

Tao²,

Yang³

et al. 2019

Int J Performability Eng

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Code and application programming interface (API) recommendation systems are important guarantees for efficient and accurate code reuse to improve the efficiency of software development. Context data plays a key role in code and API recommendation. A large amount of programming onsite data has been generated, but existing code and API recommendation systems rarely consider the context based on programming onsite data, which leads to low efficiency and poor accuracy of code and API recommendation. In this paper, we proposed a context model for code and API recommendation systems. Our context model is based on programming onsite data collected during programming. It includes four aspects: developer, project, time, and environment. Developer data is labeled data abstracted from information according to developers' programming habits and abilities, project data is information about the project, time data is information about temporal aspects of developers interacting with the project, and environment data is all environment elements used by developers during programming. Then, we collected programming onsite data in three ways: explicit collection, implicit collection, and reasoning. Lastly, we built the context model using a coarse-grained abstract model for recommendation. Our context model retains the key information in the code while eliminating redundant information that may affect the accuracy of the recommend task, and it can theoretically improve the efficiency and accuracy of recommendation.

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