| 1. |
- Chand, Sivajeet, 1998, et al.
(författare)
-
Comparing Word-Based and AST-Based Models for Design Pattern Recognition
- 2023
-
Ingår i: ACM International Conference Proceeding Series. ; , s. 44-48
-
Konferensbidrag (refereegranskat)abstract
- Design patterns (DPs) provide reusable and general solutions for frequently encountered problems. Patterns are important to maintain the structure and quality of software products, in particular in large and distributed systems like automotive software. Modern language models (like Code2Vec or Word2Vec) indicate a deep understanding of programs, which has been shown to help in such tasks as program repair or program comprehension, and therefore show promise for DPR in industrial contexts. The models are trained in a self-supervised manner, using a large unlabelled code base, which allows them to quantify such abstract concepts as programming styles, coding guidelines, and, to some extent, the semantics of programs. This study demonstrates how two language models-Code2Vec and Word2Vec, trained on two public automotive repositories, can show the separation of programs containing specific DPs. The results show that the Code2Vec and Word2Vec produce average F1-scores of 0.781 and 0.690 on open-source Java programs, showing promise for DPR in practice.
|
|
| 2. |
- Kumar Pandey, Sushant, 1990, et al.
(författare)
-
Design Patterns Understanding and Use in the Automotive Industry: An Interview Study
- 2024
-
Ingår i: Lecture Notes in Computer Science. - 0302-9743 .- 1611-3349. ; 14483, s. 301-319
-
Konferensbidrag (refereegranskat)abstract
- Automotive software is increasing in complexity, leading to new challenges for designers and developers. Design patterns, which offer reusable solutions to common design problems, are a potential way to deal with this complexity. Although design patterns have received much focus in academic publications, it is not clear how they are used in practice. This paper presents an interview-based study that explores the use of design patterns in the automotive industry. The study findings reveal how automotive practitioners view and use design patterns in their software designs. Our study revealed that industry experts have a view of design patterns which often differs from the academic views. They use design patterns in combination with architecture guidelines, principles, and frameworks. Instead of the academic focus on the design patterns, industry professionals focus on the design, architectural tactics, and standards. Such findings highlight the need for a more nuanced understanding of the concept and practical applications of design patterns within the context of industrial software engineering practices.
|
|
| 3. |
- Mishra, Alok Kumar, et al.
(författare)
-
Sensitivity of the Indian Summer monsoon rainfall to land surface schemes and model domain in a regional climate model ‘RegCM’
- 2023
-
Ingår i: Climate Dynamics. - : Springer Science and Business Media LLC. - 0930-7575 .- 1432-0894. ; 61:3-4, s. 1475-1488
-
Tidskriftsartikel (refereegranskat)abstract
- This study examines the sensitivity of regional climate model version 4.7 (RegCM4.7) to the choice of the land surface scheme (LSS) and model domain in simulating the Indian summer monsoon rainfall (ISMR). The RegCM4.7 shows improved performance in simulating the ISMR on using the Community Land Model version 4.5 (CLM) LSS as compared to the Biosphere–Atmosphere Transfer Scheme (BATS) over the most part of India. However, the (CLM) LSS as compared to the BATS over most parts of India. However, the value addition of using CLM over BATS varies spatially and also the choice of domain. For example, a more significant improvement in simulating mean precipitation is seen over the eastern (western) flank of central India on using the larger Coordinated Regional Climate Downscaling Experiment (CORDEX-SA) domain, while western and southern India shows a larger improvement in the Indian (IND) model domain. The model performance greatly improves on using the smaller Indian (IND) model domain as compared to the larger CORDEX-SA domain for the precipitation intensity greater than 15 mm/day. The best model performance in simulating the ISMR and its associated features is obtained when CLM LSS is used in combination with the IND domain (e.g., CLM IND model setup). It is found that the impact of the model domain is higher as compared to the LSS towards improved ISMR simulation. The smaller model domain size helps in the better simulation of the convective precipitation. The CORDEX-SA domain produces imperfect low-level jets (shifted westwards), which tend to reduce the moisture supply towards the Indian land region, thus leading to dry rainfall bias over the central Indian region. The BATS scheme also performs poorly in this regard as compared to the CLM scheme. The inclusion of CLM scheme in a model setup that uses the IND domain largely overcomes the problem of dry bias over the Indian region.
|
|
| 4. |
- Norgate, Marc, et al.
(författare)
-
On the heat waves over India and their future projections under different SSP scenarios from CMIP6 models
- 2024
-
Ingår i: International Journal of Climatology. - 0899-8418 .- 1097-0088.
-
Tidskriftsartikel (refereegranskat)abstract
- Thirteen Coupled Model Intercomparison Project phase 6 (CMIP6) models were employed to simulate mean, maximum, and minimum temperature across 7 homogenous temperature regions of India for both annual and summer season (June, July, and August (JJA)). The model fidelity was assessed by comparing them with observed Climate Research Unit temperature dataset. The JJA multi-model ensemble for the present (1981–2014) suggests large warm biases in the temperature. Although the models could simulate the spatial variability of the mean and maximum temperature over most of the homogeneous regions, they do not compare well for representing the temporal variability. We also found, that although different individual models have strengths and weaknesses in representing spatial and temporal temperature characteristics over India, a few of the models perform better than the others. For example, CNRM-CM6 could better represent the spatial temperature patterns however they struggle in capturing the temporal variability. HadGEM3-GC31-LL, KACE-1-0G, and UKESM1-0-LL are comparably the best-performing models for temporal temperature features over India. The annual maximum temperature during far future period is projected to increase by 1.5°C, 2.3°C, and 4.1°C for Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 respectively. At regional scales, JJA mean temperature for SSP5-8.5 revealed significant increases in Interior Peninsula (3.8°C), Western Himalaya (5.6°C), Northwest (3.9°C), West Coast (3.6°C), East Coast (3.6°C), Northeast (3.6°C), and North Central (3.8°C), highlighting the Western Himalaya's heightened sensitivity. Further, heat wave frequency is projected to rise, with the northern territories (NW, NC, NE, and part of IP) most affected, anticipating week-long heat waves affecting around 50% of India's population under stronger SSPs. Such unprecedented impacts seem to be less profound in case of abatement scenarios such as the SSP1-2.6. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to alleviate the public health impacts of climate change.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Pandey, Sushant Kumar, 1990, et al.
(författare)
-
TransDPR: Design Pattern Recognition Using Programming Language Models
- 2023
-
Ingår i: International Symposium on Empirical Software Engineering and Measurement. - 1949-3770 .- 1949-3789.
-
Konferensbidrag (refereegranskat)abstract
- Current Design Pattern Recognition (DPR) methods have limitations, such as the reliance on semantic information, limited recognition of novel or modified pattern versions, and other factors. We present an introductory DPR technique by using a Programming Language Model (PLM) called TransDPR, which utilizes a Facebook pre-trained model (TransCoder), which is a Cross-lingual programming Language Model (XLM) based on a transformer architecture. We leverage an n-dimensional vector representation of programs and apply logistic regression to learn design patterns (DPs). Our approach utilizes the GitHub repository to collect singleton and prototype DP programs written in C++ source code. Our results indicate that TransDPR achieves 90% accuracy and an F1-score of 0.88 on open-source projects. We evaluate the proposed model on two developed modules from Volvo Cars and invite the original developers to validate the prediction results.
|
|
| 8. |
|
|
