Generating Program Identifier Dictionary for Maintaining Legacy Systems

2021 ◽  
Author(s):  
Ryo Soga ◽  
Genta Koreki ◽  
Hideyuki Kanuka ◽  
Akira Ioku ◽  
Jun Maeoka
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Legacy Systems ◽  
Software Projects ◽  
Legacy System ◽  
Machine Learning Methods ◽  
Long Time ◽  
Design Documents ◽  
The Cost

Descriptions of program identifiers improve the maintainability of programs. Modern software projects maintain proper descriptions by following coding conventions. However, software projects maintained for a long time have two problems: (i) descriptions at incorrect locations and (ii) no descriptions. We propose the method of generating a identifier dictionary for managing identifiers and their descriptions, which enables developers to refer to identifier descriptions from anywhere within programs. The method involves two steps: (i) extracting identifiers and descriptions from design documents and programs and (ii) generating descriptions using information-retrieval and machine-learning methods. We applied the proposed method to COBOL programs and design documents of a legacy system that has been maintained for over 20 years as a case study. The proposed method obtained the descriptions of 83% of identifiers and reduced the cost of locating files to be modified by enhancing search keywords using the identifier dictionary. This means that the proposed method can improve the maintainability of systems maintained over many years.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Borehole Rehabilitation: A Case Study in the Dunkwa Mining Town

2020 ◽  
Vol 20 (2) ◽  
pp. 11-17
Author(s):  
A. Ewusi ◽  
J. Seidu
Keyword(s):  
Water Supply ◽  
Central Region ◽  
Pumping Test ◽  
Implementation Cost ◽  
Mining Town ◽  
Long Time ◽  
Borehole Camera ◽  
The Cost ◽  
Very High

Rehabilitation works were carried out on boreholes in the Dunkwa Mining town in the Central Region of Ghana. These works were carried out because the boreholes had lost their original yields due to clogging, corrosion and encrustation and had been abandoned for a long time. The cost of drilling a new well and assessing the productivity of the well is $4,500 which is more expensive that carrying out rehabilitation works which is cheaper, about $800. Also, the initial yields of the boreholes were very high according to the feasibility report which is not a common characteristic of the rocks in the area. Camera inspection followed by rehabilitation, pre and post pumping tests were carried out to assess whether there has been an improvement in their yield after the exercise and that the yield obtained will be adequate for a water supply design. Results show that all the boreholes had an improvement in their yields (57.19 - 259.80 %) after the rehabilitation. It can therefore be concluded that rehabilitation is effective in restoring boreholes to their original yields. Organisations drilling boreholes to communities can take advantage of rehabilitation of the existing boreholes located in the communities which are high yielding, thereby reducing project implementation cost. Keywords: Borehole Rehabilitation, Borehole Yields, Borehole Camera Inspection, Pumping Test

scholarly journals Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

2020 ◽  
Author(s):  
Wanie M. Ridwan ◽  
Michelle Sapitang ◽  
Awatif Aziz ◽  
Khairul Faizal Kushiar ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals The use of machine learning methods in the diagnosis of diseases of crops

2020 ◽  
Vol 176 ◽  
pp. 04011
Author(s):  
Sergey Korchagin ◽  
Denis Serdechny ◽  
Roman Kim ◽  
Denis Terin ◽  
Mihail Bey
Keyword(s):  
Machine Learning ◽  
Plant Diseases ◽  
Agricultural Crops ◽  
Learning Methods ◽  
Agricultural Enterprises ◽  
Machine Learning Methods ◽  
Diagnosis And Prognosis ◽  
Software Modelling ◽  
The Cost ◽  
Recurrent Type

The approach to solving the problems of diagnosis and prognosis of diseases of agricultural crops using machine learning methods is described. To solve the problem of forecasting diseases of agricultural crops, it is proposed to use a genetic algorithm in the work. The analysis of the effectiveness of the proposed method is carried out depending on the convergence rate of such parameters as the mutation coefficient and population size. To solve the problem of diagnostics of agricultural crops, it is proposed to use a recurrent type of neural network. A software modelling complex has been developed that allows solving the problems of plant diseases diagnostics and making forecasts. The results obtained can reduce the costs of agricultural enterprises by reducing the cost of diagnosing agricultural diseases.

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