Taming behavioral backward incompatibilities via cross-project testing and analysis

Chen, Lingchao; Hassan, Foyzul; Wang, Xiaoyin; Zhang, Lingming

doi:10.1145/3377811.3380436

Cited by 30 publications

(12 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Information extraction (e.g., VSM) (Nguyen et al 2012;Zhang et al 2018;Chen et al 2020;Thomas et al 2013;Fowkes et al 2016); -Classification (e.g., Support Vector Machine -SVM) (Hindle et al 2013;Le et al 2017;Liu et al 2017;Demissie et al 2020;Zhao et al 2020;Shimagaki et al 2018;Gopalakrishnan et al 2017;Thomas et al 2013); -Clustering (e.g., K-means) (Jiang et al 2019;Cao et al 2017;Liu et al 2017;Zhang et al 2016;Altarawy et al 2018;Demissie et al 2020;Gorla et al 2014); -Structured prediction (e.g., Conditional Random Field -CRF) (Ahasanuzzaman et al 2019); -Artificial neural networks (e.g., Recurrent Neural Network -RNN) (Murali et al 2017;Le et al 2017); -Evolutionary algorithms (e.g., Multi-Objective Evolutionary Algorithm -MOEA) (Blasco et al 2020;Pérez et al 2018); -Web crawling (Nabli et al 2018). Pagano and Maalej (2013) was the only study that contributed an exploration that combined LDA with another text mining technique.…”

Section: Types Of Contributionmentioning

confidence: 99%

“…However, we found that only few papers (seven out of 111) mentioned computational overhead at all. From these seven papers, five mentioned processing time (Bavota et al 2014b;Zhao et al 2020;Luo et al 2016;Moslehi et al 2016;Chen et al 2020), one paper mentioned computational requirements and some processing times (e.g., processor, data pre-processing time, LDA processing time and clustering processing time), and one paper only mention that their technique was processed in "few seconds" (Murali et al 2017). Hence, based on the reviewed studies we cannot provide broader insights into the practical applicability and potential constraints of topic modeling based on the computational overhead.…”

Section: Summary Of Findingsmentioning

confidence: 99%

See 1 more Smart Citation

Topic modeling in software engineering research

2021

View full text Add to dashboard Cite

Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from a corpus of textual documents. In software engineering, topic modeling has been used to analyze textual data in empirical studies (e.g., to find out what developers talk about online), but also to build new techniques to support software engineering tasks (e.g., to support source code comprehension). Topic modeling needs to be applied carefully (e.g., depending on the type of textual data analyzed and modeling parameters). Our study aims at describing how topic modeling has been applied in software engineering research with a focus on four aspects: (1) which topic models and modeling techniques have been applied, (2) which textual inputs have been used for topic modeling, (3) how textual data was “prepared” (i.e., pre-processed) for topic modeling, and (4) how generated topics (i.e., word clusters) were named to give them a human-understandable meaning. We analyzed topic modeling as applied in 111 papers from ten highly-ranked software engineering venues (five journals and five conferences) published between 2009 and 2020. We found that (1) LDA and LDA-based techniques are the most frequent topic modeling techniques, (2) developer communication and bug reports have been modelled most, (3) data pre-processing and modeling parameters vary quite a bit and are often vaguely reported, and (4) manual topic naming (such as deducting names based on frequent words in a topic) is common.

show abstract

Section: Types Of Contributionmentioning

confidence: 99%

Section: Summary Of Findingsmentioning

confidence: 99%

Topic modeling in software engineering research

2021

View full text Add to dashboard Cite

show abstract

“…Second, dependent tests represent a valid alternative to original tests to assess package quality, especially for packages that do not include correctly configured and runnable tests (i.e., more than half of the packages in our evaluation). In addition, dependent tests can be used to study the impact of newly found bugs [3] and the backward compatibility [19] of the target package. Finally, dependent tests can significantly increase the effectiveness of DPA tools, which can analyze a higher portion of code when compared with running only the original tests.…”

Section: Lessons Learnedmentioning

confidence: 99%

Automatically Assessing and Extending Code Coverage for NPM Packages

Sun

Rosà

Bonetta³

et al. 2021

2021 IEEE/ACM International Conference on Automation of Software Test (AST)

View full text Add to dashboard Cite

Typical Node.js applications extensively rely on packages hosted in the npm registry. As such packages may be used by thousands of other packages or applications, it is important to assess their code coverage. Moreover, increasing code coverage may help detect previously unknown issues. In this paper, we introduce TESA, a new tool that automatically assembles a test suite for any package in the npm registry. The test suite includes 1) tests written for the target package and usually hosted in its development repository, and 2) tests selected from dependent packages. The former tests allow assessing the code coverage of the target package, while the latter ones can increase code coverage by exploiting third-party tests that also exercise code in the target package. We use TESA to assess the code coverage of 500 popular npm packages. Then, we demonstrate that TESA can significantly increase code coverage by including tests from dependent packages. Finally, we show that the test suites assembled by TESA increase the effectiveness of existing dynamic program analyses to identify performance issues that are not detectable when only executing the developer's tests.

show abstract

“…Another potential usage of the clients of the library is to use the client tests to generate tests for the libraries or to verify that changes in the libraries do not break the clients. This idea is developed in a recent study performed by Chen and colleagues [14].…”

Section: A Pairs Of Libraries and Clientsmentioning

confidence: 99%

DUETS: A Dataset of Reproducible Pairs ofJava Library-Clients

Durieux,

Soto-Valero,

Baudry

2021

Preprint

View full text Add to dashboard Cite

Software engineering researchers look for software artifacts to study their characteristics or to evaluate new techniques. In this paper, we introduce DUETS, a new dataset of software libraries and their clients. This dataset can be exploited to gain many different insights, such as API usage, usage inputs, or novel observations about the test suites of clients and libraries. DUETS is meant to support both static and dynamic analysis. This means that the libraries and the clients compile correctly, they are executable and their test suites pass. The dataset is composed of open-source projects that have more than five stars on GitHub. The final dataset contains 395 libraries and 2,874 clients. Additionally, we provide the raw data that we use to create this dataset, such as 34,560 pom.xml files or the complete file list from 34,560 projects. This dataset can be used to study how libraries are used by their clients or as a list of software projects that successfully build. The client's test suite can be used as an additional verification step for code transformation techniques that modify the libraries.

show abstract

Taming behavioral backward incompatibilities via cross-project testing and analysis

Cited by 30 publications

References 45 publications

Topic modeling in software engineering research

Topic modeling in software engineering research

Automatically Assessing and Extending Code Coverage for NPM Packages

DUETS: A Dataset of Reproducible Pairs ofJava Library-Clients

Contact Info

Product

Resources

About