Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network
Authors: Alex Mathai, Sambaran Bandyopadhyay, Utkarsh Desai, Srikanth Tamilselvam
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies show that our approach is effective on monoliths of different types. and 3 Experimental Evaluation To study the efficacy of our approach, we chose four publiclyavailable monoliths namely Daytrader, Plantsby Websphere (PBW), Acme-Air and Gen App. |
| Researcher Affiliation | Industry | Alex Mathai1 , Sambaran Bandyopadhyay2 , Utkarsh Desai1 and Srikanth Tamilselvam1 1IBM Research 2Amazon {alexmathai98, samb.bandyo, utk.is.here, srikanthtamilselvam}@gmail.com |
| Pseudocode | Yes | Algorithm 1 CHGNN |
| Open Source Code | No | The paper refers to an 'extended paper 2' at 'https://arxiv.org/abs/2112.01317' but does not explicitly provide a direct link to the source code for the methodology described in this paper, nor does it state that the code is provided in supplementary materials. |
| Open Datasets | No | The paper states it uses 'four publicly-available monoliths namely Daytrader, Plantsby Websphere (PBW), Acme-Air and Gen App', but it does not provide specific links, DOIs, repositories, or formal citations with authors/year for accessing these datasets. |
| Dataset Splits | No | The paper mentions 'pre-train the heterogeneous GNN encoder and decoder' but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or defined subsets) within the main text. Details are deferred to an extended paper. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions the use of 'ADAM optimization technique' but does not provide specific version numbers for any software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | The paper states 'we use 2 message passing layers (l = 1, 2) as encoders... and next 2 message passing layers (l = 3, 4; L = 4) as decoders' and also specifies 'α1, α2, α3 and α4 are non-negative weights... we set them such that the sum of these non-negative weights always sum up to one' for the loss function. It also mentions 'We use ADAM optimization technique'. |