Can Latent Topics in Source Code Predict Missing Architectural Tactics?

Gopalakrishnan, Raghuram; Sharma, Palak; Mirakhorli, Mehdi; Galster, Matthias

doi:10.1109/icse.2017.10

Cited by 15 publications

(22 citation statements)

References 41 publications

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“…We found that most of discussed QAs (i.e., about 45% QA-AT posts) describe QA behavioral properties of a system [41]. For example, a developer mentioned that "Most unreleased resource issues result in general software reliability problems, but if an attacker can intentionally trigger a resource leak, it may be possible to launch a denial of service attack by depleting the resource pool 9 ", and in around 85% the mined QA-AT posts, developers discuss AT and QA issues using a variety of terms (see Tables 1 and 2), for example, developers used the words "workload", "memory consumption", "application crash", and "low speed" to describe Performance issues in the QA-AT posts. We counted the numbers of each QA and AT, and showed the interactions between various QAs and ATs in Fig.…”

Section: Results Of Rq21: Architectural Design Relationships Between ...mentioning

confidence: 99%

Mining Architecture Tactics and Quality Attributes knowledge in Stack Overflow

Liang

Tang

et al. 2021

Journal of Systems and Software

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Section: Results Of Rq21: Architectural Design Relationships Between ...mentioning

confidence: 99%

Mining Architecture Tactics and Quality Attributes knowledge in Stack Overflow

Liang

Tang

et al. 2021

Journal of Systems and Software

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“…Gopalakrishnan et al [75] present a bottom-up approach to identify architectural tactics. They adopted the Random Forest algorithm to capture relationships between latent topics and architectural tactics in source code of projects.…”

Section: Sotware Designmentioning

confidence: 99%

Predictive Models in Software Engineering: Challenges and Opportunities

Yang

Xia

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging and software maintenance. This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results. Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities.

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“…The majority of the studies mined a large number of repositories to construct their own dataset. Specifically, Gopalakrishnan et al [109] examined 116,000 open-source systems to identify correlations between the latent topics in source code and the usage of architectural developer tactics (such as authentication and load-balancing). Han et al [128], Han et al [129] trained and tested their system by sampling 4,919 source code lines from open-source projects.…”

Section: Data Collectionmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

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Context:The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and vulnerabilities detection. A large number of studies poses challenges to the community to understand the current landscape. Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we carried out an extensive literature search and identified 364 primary studies published between 2002 and 2021. We summarize our observations and findings with the help of the identified studies. Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task, and summarize the employed machine learning techniques. Additionally, we collate a comprehensive list of available datasets and tools useable in this context. Finally, we summarize the perceived challenges in this area that include availability of standard datasets, reproducibility and replicability, and hardware resources. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software maintenance tools; Software post-development issues; Maintaining software; • Computing methodologies → Machine learning.

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Can Latent Topics in Source Code Predict Missing Architectural Tactics?

Cited by 15 publications

References 41 publications

Mining Architecture Tactics and Quality Attributes knowledge in Stack Overflow

Mining Architecture Tactics and Quality Attributes knowledge in Stack Overflow

Predictive Models in Software Engineering: Challenges and Opportunities

A Survey on Machine Learning Techniques for Source Code Analysis

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