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Feature Selection for Agile Development Through Data Mining Techniques

Thesis Info

Access Option

External Link

Author

Waqas Jawaid

Institute

Virtual University of Pakistan

Institute Type

Public

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2014

Thesis Completion Status

Completed

Subject

Software Engineering

Language

English

Link

http://vspace.vu.edu.pk/detail.aspx?id=7

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676720944171

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Traditionally software development has been performed by following a phased model. This model is called the waterfall (or traditional) model, in which the software development life cycle is divided into distinct phases i.e. requirements elicitation, software development, testing, and maintenance, which are followed in a defined order. The waterfall model creates heavy documentation, and most often results in huge rework and cost overruns because customer does not get the visibility of the project until it is very late. As an alternative to the documentation driven, heavyweight software development processes, many lightweight methodologies were created by software practitioners, e.g. crystal methodologies, feature driven development (FDD), scrum, extreme programming (XP) etc. The lightweight methodologies are called agile methodologies, and the development that is done following this model is called agile development. The agile methodologies follow the practices that add value to customers, and accept changes at any time during the development. From its inception, the agile development has been observed to be highly successful and it is used in most of the software companies now. However if a company wants to start using the agile development, then it is very difficult for it to choose which agile practices or features it should follow, because there are many practices for agile development, from which the company must choose which ones it would use. In this work the most important success factors will be extracted from the agile development practices that are successful in the software industry (with reference to the software practitioners in Pakistan). Through literature survey, the candidate factors will be selected, and then a survey will be conducted (using online survey forms) from the software practitioners of Pakistan, to obtain data about which agile practices are the most successful practices? The survey results will be analyzed using state of the art data mining techniques (e.g. dimension reduction techniques, clustering techniques, and regression modeling) on multiple dimensions (extent of usage, extent of benefits) to find out which practices are most successful among the software practitioners? The most useful success factors/features of agile development will be extracted on the basis of the obtained results.
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Booster Dose of COVID 19 and Ethical Issues

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Integrated Nitrogen Management With and Without Beneficial Microbes in Spring Maize and its Carry over Effect on Subsequent Mung Bean

The effectiveness of beneficial microbes in improving soil fertility and crop productivity may vary with soil organic matter and nitrogen contents. For this purpose, field trials were conducted at the Agronomy Research Farms of the University of Agriculture Peshawar to evaluate the effect of beneficial microbes, organic and inorganic N ratios and N levels in spring maize and mung bean cropping system in semi-arid alkaline calcareous soil condition of Peshawar during 2014 and 2015. Maize was sown in spring followed by mung bean in summer. The experiment was consisted of three factors i.e., beneficial microbes (BM) (with BM and without BM), organic and inorganic N sources (R) (0:100, 25:75, 50:50, 75:25 and 100:0), desired nitrogen (N) levels (100, 150 and 200 kg N ha-1) and control. The experiment was carried out in randomized complete block design (RCBD) with 3 replications. Plot size was 4.2 m x 4 m having six rows in maize and 12 rows in subsequent mung bean. Row to row distance for maize and mung bean was 70 and 35 cm respectively. Mung bean was sown without application of the cited treatments for carry over only. Based on the average results of the two years, BM significantly enhanced plant height (220 cm), SPAD value (62.53), days to tasselling (63.3 d), silking (66.9 d), maturity (101 d), leaf rea (437 cm2), LAI (3.47), ears m-2 (7.0), grains ear-1 (576), thousand grains weight (269 g), biological yield (16543 kg ha-1), grain yield (5438 kg ha-1), harvest index (32.9 %), soil organic matter (1.04 %), stover N (0.77 %), grain N (1.64 %), grain protein (10.23 %), stover N uptake (76.3 kg ha-1), grains N uptake (89.8 kg ha-1), total N uptake (127 kg ha-1) and N use efficiency (24.5 kg grains kg-1 N supply). Higher soil mineral N (47.7 mg kg-1) was observed without application of BM. Application of organic and inorganic N in 50:50 R significantly improved plant height (222 cm), SPAD value (63.68), days to tasselling (63.1 d), days to silking (66.9 d), days to maturity (100 d), leaf area (456 cm2), LAI (3.61), ear m-2 (7.0), grains ear-1 (554), thousand grains weight (266.3 g), biological yield (16792 kg ha-1), grain yield (5732 kg ha-1), harvest index (34.1 %), stover N (0.81 %), grain N (1.68 %), grain protein content (10.5 %), stover N uptake (81.06 kg ha-1), grains N uptake (97.17 kg ha-1), total N uptake (131.9 kg ha-1) and N use efficiency (25.9 kg grains kg-1 N supply). Higher soil organic matter (1.21 %) was observed with application of full dose of N applied from organic source. Soil mineral N (50.2 mg kg-1) and soil total N (0.085%) were observed with application of organic and inorganic N in 75:25 R. Application of N @150 kg ha-1 significantly increased SPAD value (61.9), leaf area (445 cm2), LAI (3.59), ears m-2 (6.9), grains ear- 1 (548), thousand grains weight (266.2 g), grain yield (5228 kg ha-1), harvest index (32.7 %), grain N (1.63 %), grain protein (10.19 %) and grains N uptake (85.82 kg hav 1). Higher plant height (220 cm), days to tasselling (63.9 d), silking (67.3 d), and maturity (102 d), biological yield (16589 kg ha-1), soil organic matter (1.21 %), soil total N (0.082%), stover N (0.89 %), stover N uptake (93.13 kg ha-1) and total N uptake (144.03 kg ha-1) were observed with application of 200 kg N ha-1. Similarly, N use efficiency (26.13 kg grains kg-1 N supply) were recorded with application of N @ 100 kg ha-1. In mung bean, higher pods m-2 (214), grains pod-1 (11), thousand grains weight (50.8 g), grain yield (918 kg ha-1), biological yield (5896 kg ha-1) and harvest index (17.12 %) were observed with the residual effect of BM. However, more pods m-2 (231), grains pod-1 (11.2), thousand grains weight (50.8 g), grain yield (927 kg ha-1) and biological yield (6141 kg ha-1) were obtained with the residual effect of organic and inorganic N in 75:25 and 50:50 R while higher harvest index (17.7 %) was observed with organic and inorganic N in 50:50 R. Higher grains pod-1 (10.8), thousand grains weight (51.24 g), grain yield (917 kg ha-1) and biological yield (5935 kg ha-1) were observed with the residual effect of 150 kg N ha-1 while higher harvest index (17.12%) was observed with residual effect of 100 kg N ha-1. On the basis of above results, it was concluded that BM in combination with N @ 150 kg N ha-1 in organic and inorganic ratio of 50:50 performed better and are, therefore, recommended for higher productivity in spring maize-mung bean cropping system in the agro-climatic conditions of Peshawar Pakistan.