78 research outputs found
From RSSE to BotSE: Potentials and Challenges Revisited after 15 Years
Both recommender systems and bots should proactively and smartly answer the
questions of software developers or other project stakeholders to assist them
in performing their tasks more efficiently. This paper reflects on the
achievements from the more mature area of Recommendation Systems in Software
Engineering (RSSE) as well as the rising area of Bots in Software Engineering
(BotSE). We discuss the similarities and differences, briefly review current
state of the art, and highlight three particular areas, in which the full
potential is yet to be tapped: a more socio-technical context awareness,
assisting knowledge sharing in addition to knowledge access, as well as
covering repetitive or stimulative scenarios related to requirements and
user-developer interaction
Harnessing deep learning algorithms to predict software refactoring
During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring predictio
Identifying developers’ habits and expectations in copy and paste programming practice
Máster Universitario en Investigación e Innovación en
Inteligencia Computacional y Sistemas InteractivosBoth novice and experienced developers rely more and more in external
sources of code to include into their programs by copy and paste code snippets. This
behavior differs from the traditional software design approach where cohesion was
achieved via a conscious design effort. Due to this fact, it is essential to know how copy
and paste programming practices are actually carried out, so that IDEs (Integrated
Development Environments) and code recommenders can be designed to fit with
developer expectations and habit
Enriched Event Streams: A General Platform For Empirical Studies On In-IDE Activities Of Software Developers
Current studies on software development either focus on the change history of source code from version-control systems or on an analysis of simplistic in-IDE events without context information. Each of these approaches contains valuable information that is unavailable in the other case. This work proposes enriched event streams, a solution that combines the best of both worlds and provides a holistic view on the in-IDE software development process. Enriched event streams not only capture developer activities in the IDE, but also specialized context information, such as source-code snapshots for change events. To enable the storage of such code snapshots in an analyzable format, we introduce a new intermediate representation called Simplified Syntax Trees (SSTs) and build CARET, a platform that offers reusable components to conveniently work with enriched event streams. We implement FeedBaG++, an instrumentation for Visual Studio that collects enriched event streams with code snapshots in the form of SSTs and share a dataset of enriched event streams captured in an ongoing field study from 81 users and representing 15K hours of active development. We complement this with a dataset of 69M lines of released source code extracted from 360 GitHub repositories. To demonstrate the usefulness of our platform, we use it to conduct studies on the in-IDE development process that are both concerned with source-code evolution and the analysis of developer interactions. In addition, we build recommendation systems for software engineering and analyze and improve current evaluation techniques
Recommender systems in model-driven engineering: A systematic mapping review
Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of researchThis work has been funded by the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie
Grant Agreement No. 813884 (Lowcomote [134]), by the Spanish
Ministry of Science (projects MASSIVE, RTI2018-095255-B-I00, and
FIT, PID2019-108965GB-I00) and by the R&D programme of Madrid
(Project FORTE, P2018/TCS-431
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