International Mining Software Repositories, MSR
2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)
25-31 May 2019
Showing 1-25 of 90
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The proceedings of this conference will be available for purchase through Curran Associates.
Mining Software Repositories (MSR), 2019 IEEE/ACM 16th International Conference on
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[Title page i]
Provides an abstract of the keynote presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.Show More
The Impact of Systematic Edits in History Slicing
Publication Year: 2019,Page(s):555 - 559
Cited by: Papers (1)
While extracting a subset of a commit history, specifying the necessary portion is a time-consuming task for developers. Several commit-based history slicing techniques have been proposed to identify dependencies between commits and to extract a related set of commits using a specific commit as a slicing criterion. However, the resulting subset of commits become large if commits for systematic edi...Show More
Scalable Software Merging Studies with MERGANSER
Publication Year: 2019,Page(s):560 - 564
Cited by: Papers (1)
Software merging researchers constantly need empirical data of real-world merge scenarios to analyze. Such data is currently extracted through individual and isolated efforts, often with non-systematically designed scripts that may not easily scale to large studies. This hinders replication and proper comparison of results. In this paper, we introduce MERGANSER, a scalable and easy-to-use tool for...Show More
Standing on Shoulders or Feet? The Usage of the MSR Data Papers
Publication Year: 2019,Page(s):565 - 576
Cited by: Papers (8)
Introduction: The establishment of the Mining Software Repositories (MSR) Data Showcase conference track has encouraged researchers to provide more data sets as a basis for further empirical studies. Objectives: Examine the usage of the data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Methods: Data track papers were collected from the MSR Data Showcas...Show More
Boa Meets Python: A Boa Dataset of Data Science Software in Python Language
Publication Year: 2019,Page(s):577 - 581
Cited by: Papers (19)
The popularity of Python programming language has surged in recent years due to its increasing usage in Data Science. The availability of Python repositories in Github presents an opportunity for mining software repository research, e.g., suggesting the best practices in developing Data Science applications, identifying bug-patterns, recommending code enhancements, etc. To enable this research, we...Show More
A Benchmark of Data Loss Bugs for Android Apps
Oliviero Riganelli;Marco Mobilio;Daniela Micucci;Leonardo Mariani
Publication Year: 2019,Page(s):582 - 586
Cited by: Papers (4)
Android apps must be able to deal with both stop events, which require immediately stopping the execution of the app without losing state information, and start events, which require resuming the execution of the app at the same point it was stopped. Support to these kinds of events must be explicitly implemented by developers who unfortunately often fail to implement the proper logic for saving a...Show More
RapidRelease - A Dataset of Projects and Issues on Github with Rapid Releases
Publication Year: 2019,Page(s):587 - 591
Cited by: Papers (5)
In the recent years, there has been a surge in the adoption of agile development model and continuous integration (CI) in software development. Recent trends have reduced average release cycle lengths to as low as 1-2 weeks, leading to an extensive number of studies in release engineering. Open-source development (OSD) has also witnessed a rapid increase in release rates, however, no large dataset...Show More
ConPan: A Tool to Analyze Packages in Software Containers
Ahmed Zerouali;Valerio Cosentino;Gregorio Robles;Jesus M. Gonzalez-Barahona;Tom Mens
Publication Year: 2019,Page(s):592 - 596
Cited by: Papers (8)
Deploying software packages and services into containers is a popular software engineering practice that increases portability and reusability. Docker, the most popular containerization technology, helps DevOps practitioners in their daily activities. Despite being successfully and increasingly employed, containers may include buggy and vulnerable packages that put at risk the environments in whic...Show More
An Empirical History of Permission Requests and Mistakes in Open Source Android Apps
Gian Luca Scoccia;Anthony Peruma;Virginia Pujols;Ben Christians;Daniel Krutz
Publication Year: 2019,Page(s):597 - 601
Cited by: Papers (6)
Android applications (apps) rely upon proper permission usage to ensure that the user's privacy and security are adequately protected. Unfortunately, developers frequently misuse app permissions in a variety of ways ranging from using too many permissions to not correctly adhering to Android's defined permission guidelines. The implications of these permissionissues (possible permission problems) ...Show More
Author index
SCOR: Source Code Retrieval with Semantics and Order
Publication Year: 2019,Page(s):1 - 12
Cited by: Papers (11)
Word embeddings produced by the word2vec algorithm provide us with a strong mechanism to discover relationships between the words based on the degree to which they are contextually related to one another. In and of itself, algorithms like word2vec do not give us a mechanism to impose ordering constraints on the embedded word representations. Our main goal in this paper is to exploit the semantic w...Show More
PathMiner: A Library for Mining of Path-Based Representations of Code
Vladimir Kovalenko;Egor Bogomolov;Timofey Bryksin;Alberto Bacchelli
Publication Year: 2019,Page(s):13 - 17
Cited by: Papers (24)
One recent, significant advance in modeling source code for machine learning algorithms has been the introduction of path-based representation - an approach consisting in representing a snippet of code as a collection of paths from its syntax tree. Such representation efficiently captures the structure of code, which, in turn, carries its semantics and other information. Building the path-based re...Show More
Import2vec: Learning Embeddings for Software Libraries
Publication Year: 2019,Page(s):18 - 28
Cited by: Papers (21)
We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogical reasoning. We apply word embedding techniques from natural language processing...Show More
Semantic Source Code Models Using Identifier Embeddings
Publication Year: 2019,Page(s):29 - 33
Cited by: Papers (16)
The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods. In th...Show More
DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction
Thong Hoang;Hoa Khanh Dam;Yasutaka Kamei;David Lo;Naoyasu Ubayashi
Publication Year: 2019,Page(s):34 - 45
Cited by: Papers (142)
Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction - aka. Just-In-Time (JIT) defect prediction - has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones....Show More
Lessons Learned from Using a Deep Tree-Based Model for Software Defect Prediction in Practice
Hoa Khanh Dam;Trang Pham;Shien Wee Ng;Truyen Tran;John Grundy;Aditya Ghose;Taeksu Kim;Chul-Joo Kim
Publication Year: 2019,Page(s):46 - 57
Defects are common in software systems and cause many problems for software users. Different methods have been developed to make early prediction about the most likely defective modules in large codebases. Most focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and multiple levels of ...Show More
Empirical Study in using Version Histories for Change Risk Classification
Publication Year: 2019,Page(s):58 - 62
Many techniques have been proposed for mining software repositories, predicting code quality and evaluating code changes. Prior work has established links between code ownership and churn metrics, and software quality at file and directory level based on changes that fix bugs. Other metrics have been used to evaluate individual code changes based on preceding changes that induce fixes. This paper ...Show More
[Title page iii]
Snoring: A Noise in Defect Prediction Datasets
Publication Year: 2019,Page(s):63 - 67
Cited by: Papers (13)
In order to develop and train defect prediction models, researchers rely on datasets in which a defect is often attributed to a release where the defect itself is discovered. However, in many circumstances, it can happen that a defect is only discovered several releases after its introduction. This might introduce a bias in the dataset, i.e., treating the intermediate releases as defect-free and t...Show More
Exploring Word Embedding Techniques to Improve Sentiment Analysis of Software Engineering Texts
Publication Year: 2019,Page(s):68 - 78
Cited by: Papers (20)
Sentiment analysis (SA) of text-based software artifacts is increasingly used to extract information for various tasks including providing code suggestions, improving development team productivity, giving recommendations of software packages and libraries, and recommending comments on defects in source code, code quality, possibilities for improvement of applications. Studies of state-of-the-art s...Show More
Cleaning StackOverflow for Machine Translation
Publication Year: 2019,Page(s):79 - 83
Cited by: Papers (4)
Generating source code API sequences from an English query using Machine Translation (MT) has gained much interest in recent years. For any kind of MT, the model needs to be trained on a parallel corpus. In this paper we clean StackOverflow, one of the most popular online discussion forums for programmers, to generate a parallel English-Code corpus from Android posts. We contrast three data cleani...Show More
Predicting Good Configurations for GitHub and Stack Overflow Topic Models
Publication Year: 2019,Page(s):84 - 95
Cited by: Papers (23)
Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic modelling is frequently used as a text-mining tool for the discovery of hidden semantic structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used topic model that ...Show More
A Dataset of Parametric Cryptographic Misuses
Anna-Katharina Wickert;Michael Reif;Michael Eichberg;Anam Dodhy;Mira Mezini
Publication Year: 2019,Page(s):96 - 100
Cited by: Papers (5)
Cryptographic APIs (Crypto APIs) provide the foundations for the development of secure applications. Unfortunately, most applications do not use Crypto APIs securely and end up being insecure, e.g., by the usage of an outdated algorithm, a constant initialization vector, or an inappropriate hashing algorithm. Two different studies [1], [2] have recently shown that 88% to 95% of those applications ...Show More
Does UML Modeling Associate with Lower Defect Proneness?: A Preliminary Empirical Investigation
Adithya Raghuraman;Truong Ho-Quang;Michel R. V. Chaudron;Alexander Serebrenik;Bogdan Vasilescu
Publication Year: 2019,Page(s):101 - 104
Cited by: Papers (7)
The benefits of modeling the design to improve the quality and maintainability of software systems have long been advocated and recognized. Yet, the empirical evidence on this remains scarce. In this paper, we fill this gap by reporting on an empirical study of the relationship between UML modeling and software defect proneness in a large sample of open-source GitHub projects. Using statistical mo...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Mining Software Repositories (MSR), 2019 IEEE/ACM 16th International Conference on
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