EdSketch: execution-driven sketching for Java

Jiang, Hua; Zhang, Yushan; Zhang, Yuqun; Khurshid, Sarfraz

doi:10.1007/s10009-019-00512-8

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…In contrast, because our approach derives mocks directly from the input's assertions, we need not consider the code itself when modeling it. Hua et al [11] modularize the synthesis of library calls through execution of actual partial programs. In contrast, we attempt to avoid called functions entirely by relying on their inferred specifications.…”

Section: Related Workmentioning

confidence: 99%

Program Sketching by Automatically Generating Mocks from Tests

Bragg

Foster

Roux

et al. 2021

Computer Aided Verification

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Sketch is a popular program synthesis tool that solves for unknowns in a sketch or partial program. However, while Sketch is powerful, it does not directly support modular synthesis of dependencies, potentially limiting scalability. In this paper, we introduce Sketcham, a new technique that modularizes a regular sketch by automatically generating mocks—functions that approximate the behavior of complete implementations—from the sketch’s test suite. For example, if the function f originally calls g, Sketcham creates a mock $$g_m$$ g m from g’s tests and augments the sketch with a version of f that calls $$g_m$$ g m . This change allows the unknowns in f and g to be solved separately, enabling modular synthesis with no extra work from the Sketch user. We evaluated Sketcham on ten benchmarks, performing enough runs to show at a 95% confidence level that Sketcham improves median synthesis performance on six of our ten benchmarks by a factor of up to 5$$\times $$ × compared to plain Sketch, including one benchmark that times out on Sketch, while exhibiting similar performance on the remaining four. Our results show that Sketcham can achieve modular synthesis by automatically generating mocks from tests.

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Section: Related Workmentioning

confidence: 99%

Program Sketching by Automatically Generating Mocks from Tests

Bragg

Foster

Roux

et al. 2021

Computer Aided Verification

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“…While microservice systems have been widely studied under various domains [36]- [39], there exists only one previous work that refers to Stack Overflow for extraction of topics in microservice development. Bandeira et al [20] implemented a topic model, which is an unsupervised machine learning that detects word or phrase patterns in Stack Overflow posts to build a general taxonomy of subjects on microservices.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study on Microservice Software Development

Ramírez

Mera‐Gómez

Bahsoon

et al. 2021

2021 IEEE/ACM Joint 9th International Workshop on Software Engineering for Systems-of-Systems and 15th Workshop on Distributed

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Microservice is an approach to software development, in which an application is designed and constructed to maximise the benefits of modularisation. This approach improves the testing of applications, where modularization can limit the propagation of bugs and facilitates their detection. Though the microservices paradigm has the potentials to ease the automation of bugs detection and fixing, the process is still less understood by the microservices community. To bridge this gap and accelerate such an understanding, we extracted posts from Stack Overflow to identify the most commonly discussed issues in microservice development and testing and we categorised the concerns. Our results indicate that (i) missing parameters and operations are the most common concerns in service routing; (ii) wrong versions of libraries, annotations, protocols, and clusters appear as the main concerns on service discovery; (iii) the absence of authorisation operation and web tokens exposed for a long time are the main concerns related to service authentication and authorisation; and (iv) the absence of configuration parameters for cache and inadequate patterns for long-running transactions emerge as trending concerns in service invocation. We analyse our findings and provide suggestions for future research.

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“…Extracted data with respect to the research questions (RQs), inclusion criteria (IC), exclusion criteria (EC), and categorization scheme. [24][25][26][27][28] Different sketching synthesis techniques for expressing user intent with annotated programs, e.g., execution-driven SAT/SMT-based and domain-specific rule sketching. User intent is expressed using a program sketch with some holes.…”

Section: Classification Schemementioning

confidence: 99%

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Subahi

2020

Computers

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Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

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EdSketch: execution-driven sketching for Java

Cited by 10 publications

References 20 publications

Program Sketching by Automatically Generating Mocks from Tests

Program Sketching by Automatically Generating Mocks from Tests

An Empirical Study on Microservice Software Development

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

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