Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using Genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

Bug Prioritization Using Average One Dependence Estimator

Automation software need to be continuously updated by addressing software bugs contained in their repositories.However,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and importance.Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical bugs.Therefore,bug report prioritization is vital.This study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of attributes.The more the number of attributes,the more the severity.The proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation coefficient.Results of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)models.Eclipse and Mozilla datasetswere used as the sources of bug reports.The proposed model improved the bug repository management and out-performed the SVM and NB models.Additionally,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost.

Test Case Prioritization in Unit and Integration Testing:A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

Prioritization of Semi-Arid Agricultural Watershed Using Morphometric and Principal Component Analysis, Remote Sensing, and GIS Techniques, the Zerqa River Watershed, Northern Jordan

Remote sensing and GIS techniques were employed for prioritization of the Zerqa River watershed. Forty-three 4th order sub-watersheds were prioritized based on morphometric and Principal Component Analysis (PCA), in order to examine the effectiveness of morphometric parameters in watershed prioritization. A comparison has been carried out between the results achieved through applying the two methods of analysis (morphometric and PCA). Afterwards, suitable measures are proposed for soil and water conservation. Topo sheets and ASTER DEM have been employed to demarcate the 43 sub-watersheds, to extract the drainage networks, and to compile the required thematic maps such as slope categories and elevation. LANDSAT 8 image (April-2015) is employed to generate land use/cover maps using ENVI (v 5.1) software. The soil map of the watershed has been digitized using Arc GIS software. Prioritization of the 43 sub-watersheds was performed using ten linear and shape parameters, and three parameters which are highly correlated with components 1 and 2. Subsequently, different sub-watersheds were prioritized by ascribing ranks based on the calculated compound parameters (Cp) using the two approaches. Comparison of the results revealed that prioritization of watersheds based on morphometric analysis is more consistent and serves for better decision making in conservation planning as compared with the PCA approach. The recommended soil conservation measures are prescribed in accordance with the specified priority, in order to avoid undesirable effects on land and environment. Sub-watersheds classified under high priority class are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. It is expected that decision makers will pay sufficient attention to the present results/information, activate programs encouraging soil conservation, integrated watershed management, and will continue working on the afforestation of the government-owned sloping lands. Such a viable approach can be applied at different parts of the rainfed highland areas to minimize soil erosion loss, and to increase infiltration and soil moisture in the soil profile, thus, reducing the impact of recurrent droughts and the possibility of flooding hazards.

SWPT:An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors

The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources.Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner.Hence,this study aimed at developing a user-friendly geographic information system(GIS)tool,Sub-Watershed Prioritization Tool(SWPT),using the Python programming language to decrease any possible uncertainty.It used geospatial-statistical techniques for analyzing morphometric and topohydrological factors and automatically identifying critical and priority sub-watersheds.In order to assess the capability and reliability of the SWPT tool,it was successfully applied in a watershed in the Golestan Province,Northern Iran.Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds.It provided a cost-effective approach for prioritization of sub-watersheds.Therefore,the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.

A Remote Sensing and GIS Approach for Prioritization of Wadi Shueib Mini-Watersheds (Central Jordan) Based on Morphometric and Soil Erosion Susceptibility Analysis

Recently watershed prioritization has become a pragmatic approach for watershed management and natural resources development. Wadi Shueib is a Jordan Rift valley and covers an area of 177.8 km2. The upper catchment is of dry Mediterranean climate, whereas the lower part is arid. The drainage network is sub-dendritic pattern, with a trellis pattern developed due to the influence of W. Shueib structure. Fourteen mini-watersheds were delineated and designated as (MW 1 to MW 14) for prioritization purposes. Morphometric analysis, and soil erosion susceptibility analysis were conducted, and their values were calculated for each mini-watersheds. Based on value/relationship with erodibility, different prioritization ranks were ascribed following the computation of compound factors. Based on morphometric and soil erosion susceptibility analysis, and the resultant ranks, the mini-watersheds have been classified into four categories in relation to their priority for soil conservation measures: very high, high, moderate, and low. It is found that 64.3% of the 3rd order mini-watersheds are classified in the categories of very high and high priority. Based on soil erosion susceptibility analysis, three mini-watersheds are of very high priority and three are of high priority. The integration of morphometric and soil erosion susceptibility methods shows that mini-watersheds no.2 and no.3 are common mini-watersheds, and can be classified in the class of moderate and low priority respectively. By contrast, two mini-watersheds (no.8 and no.13) are categorized in the class of high priority based on morphometric analysis, and are classified in the category of very high priority based on soil erosion susceptibility analysis. Similarly, mini-watershed no.14 can be placed in the category of very high priority based on morphometric analysis, and ranks in the category of high priority based on soil erosion susceptibility analysis. With reference to the integration of the two methods of prioritization, it can be concluded that most of the mini-watersheds can be categorized in the classes moderate, high, and very high priority. Consequently, the entire W. Shueib watershed must be prioritized for soil and water conservation to ensure future sustainable agriculture and development of natural resources.

Prioritization of Water Resources Management Problems in North Central Nigeria Using Rapid Impact Assessment Matrix (RIAM)

This study employed the Rapid Impact Assessment Matrix (RIAM) to prioritize the water resources management problems in the North Central Nigeria. This was done through the assessment of the status of water resources management in the region, evaluation of existing policy and strategy of water management, identification of the management problems and the prioritization with RIAM. The stakeholders identified water resources management problems, ranked them in other of severity in different categories and also evaluated them using the RIAM techniques in the administered questionnaire. Eleven problems were analyzed based on the physical/chemical, biological/ecological, social/cultural and economic/operational factors using several impact indicators. Scores were assigned, the RIAM models applied and the averages taken to arrive at the final assessment scores. The two major water resources management problems identified are: 1) inadequate funds for further agricultural, hydroelectric, navigation and industrial development;2) poor data collection and banking. These problems were prioritized by RIAM in order of severity for urgent intervention. The RIAM technique has made a key contribution to the prioritization of water resources management by providing insights into urgent problems according to stakeholders and thus guides the policy maker in appropriate decision making.

Prioritization of W. Mujib Catchment (South Jordan) through Morphometric and Discriminant Analysis, GIS, and RS Techniques

GIS and remote sensing were utilized for prioritizing the W. Mujib catchment. Fifty three fourth-order sub-watersheds were prioritized based on morphometric analysis of linear and shape parameters. ASTER DEM (v.2), topographical maps, and Arc GIS (10.1) software, have been employed to delineate the 53 sub-basins, to extract the drainage networks, and to compute the required basic, linear, and shape parameters, and to compile the necessary thematic maps such as elevation and slope categories. The land use/land cover map was generated using ERDAS Imagine (2015), LANDSAT 8 image, and supervised classification (Maximum Likelihood Method). Soil map was digitized using the Arc GIS tool. Each sub-basin is prioritized by assigning ranks based on the calculated compound parameter (Cp). The final score for each sub-basin is ascribed as per erosion threat. The 53 sub-watersheds were grouped into four categories of priority: very high (15 sub-basins, 28.3% of the total), high (17 sub-basins, 32% of the total), moderate (16 sub-basins, 30.2% of the total), and low (5 sub-basins, 9.5% of the total). Sub-basins categorized as very high and high priority (60.3% of the total) are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. The validity of the prioritized four groups was tested statistically by means of Discriminant Analysis (DA), and a significant difference was found between the four priority classes. A relatively complete separation exists between the recognized priority classes;thus, they are statistically valid, distinct, and different from each other. The present results intend to help decision makers pay sufficient attention to soil and water conservation programs, and to encourage tree plantation over the government-owned sloping land. Such procedures are essential in order to minimize soil erosion loss, and to increase soil moisture on farms, thus, reducing the impact of recurrent droughts and the possibility of flooding downstream.

Prioritization of Sub-Watersheds in a Large Semi-Arid Drainage Basin (Southern Jordan) Using Morphometric Analysis, GIS, and Multivariate Statistics

GIS-based morphometric analysis was employed to prioritize the W. Mujib-Wala watershed southern Jordan. Seventy six fourth-order sub-watersheds were prioritized using morphometric analysis of ten linear and shape parameters. Each sub-watershed is prioritized by designated ranks based on the calculated compound parameter (Cp). The total score for each sub-basin is assigned as per erosion threat. The 76 sub-basins were grouped into four categories of priority: very high (12 sub-basins, 15.8% of the total), high (32 sub-watersheds, 42.1% of the total), moderate (25 sub-watersheds, 32.9% of the total), and low (7 sub-watersheds, 9.2% of the total). Sub-watersheds categorized as very high and high are subjected to high erosion risk, thus creating an urgent need for applying soil and water conservation measures. The relative diversity in land use practices and land cover, including variation in slope and soil types, are considered in proposing suitable conservation structures for sub-watersheds connected to each priority class. The adaptation of soil conservation measures priority-wise will reduce the erosivity effect on soil loss;while increasing infiltration rates;and water availability in soil profile. Principal component analysis (PCA) reduces the basic parameters and erosion risk parameters to three components, explaining 88% of the variance. The relationships of these components to the basic and erosion risk parameters were evaluated, and then the degree of inter-correlation among the morphometric parameters was explored. The verification of priority classes obtained through morphometric analysis was tested using Discriminant Analysis (DA). The results show a complete separation existing between the identified priority classes. Thus, soil erosion risk and geomorphic conditions are found entirely different from one class to another. The present results are intended to help decision makers to plan for efficient soil and water conservation measures to achieve future agricultural sustainability in the rainfed highlands of Jordan.

Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand.This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its customers.Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection criteria.These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy Process.Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and risk.Subsequently,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection criteria.Three experts with seven and three and ten years of experience have participated in the study.Findings of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred customers.Moreover,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are consistent.Furthermore,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision.Unlike traditional supply chain problems of supplier selection,this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria.

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is proposed.First,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea progamming.Secondly,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test cases.Five typical open source projects are selected as benchmark programs.Branch and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

Diversity,Distribution and Prioritization of Fodder Species for Conservation in Kullu District,Northwestern Himalaya,India

In the Indian Himalayan Region predominantly rural in character, livestock is one of the main sources of livelihood and integral part of the economy. Livestock mostly rely on fodder from wild. The diversity, distribution, utilization pattern, nativity, endemism, rarity, seasonality of availability, nutritive values, perceived economic values and pressure use index of livestock have not been studied. The present study attempts to enumerate 150 species of fodder representing trees (51 spp.), shrubs (54 spp.) and herbs (45 spp.). Poaceae (19 spp.) and Fabaceae (13 spp.) amongst families and Salix (6 spp.), Ficus, Clematis, and Desmodium (5 spp., each) amongst genera are rich in species. Maximum species were found in the 1801 ~ 2600 m zone, and the remaining two zones showed relatively low diversity. Out of the 150 species, 109 are used in summer, 5 winter and 36 throughout year. During rainy season, mostly grasses are used as fodder. Only 83 species are native to the Himalayan region, one species, Strobilanthus atropuroureus is endemic and 35 species are near endemic. The nutritive values of the fodder species were reviewed, and economic values and status of the species were also assessed. The pressure use index of the species was calculated on the basis of cumulative values of the utilization pattern, altitudinal distribution, availability, status, nativity and endemism. Amongst the species, Grewia oppositifoilia, Morus serrata, Indigofera heterantha, Quercus leucotrichphora, Ulmus villosa, U. wallichiana and Aesculus indica showed highest PUI indicating high preference and pressure. Season wise prioritization of the species for different altitudinal zones has been done. Appropriate strategy and action plan have been suggested for the conservation and management of fodder species.

Prioritization of W. Alarab Sub-Watersheds (North Jordan) for Conservation Measures Using RS, GIS, and Multi-Criteria Analysis

Soil erosion and high sediment flow are of eminent environmental concern in Wadi Alarab catchment, northern Jordan. The objective of this research is to conduct a prioritization scheme using RS, GIS, and multi-criteria analysis approach based on morphometric analysis, land use/land cover (LULC) change analysis, and soil loss modeling based on RUSLE model factors. ASTER GDEM and Arc GIS were utilized to delineate watersheds and extract the drainage networks using the Arc Hydro tool. Five basic morphometric parameters, five linear and five shape parameters, six LULC classes, and five soil erosion risk classes are applied to prioritize 13 sub-watersheds connected to W. Alarab basin. LANDSAT images were subjected to supervised classification (the Maximum Likelihood Method) to determine land use/cover changes and to establish the LULC map/layer. Soil erosion risk classes were estimated using the RULSE model. RULSE factors (R, K, L, S, C, and P) were calculated in a GIS environment, then multiplied together so as to estimate soil loss (ton·ha-1·yr-1) and to establish a soil erosion risk map for the entire watershed and the thirteen sub-watersheds. A GIS-based integration of the three layers compiled for each criterion reveals that six sub-watersheds (1, 5, 8, 9, 10, and 11) are categorized under low priority. Further, three sub-basins (4, 12, and 13) are fall under moderate priority, and four sub-basins (2, 3, 6, and 7) are designated as of high priority. It is obvious that 53.8% of these sub-basins must be prioritized immediately for soil and conservation measures. The validity of the achieved priority classes was tested statistically using Discriminant Analysis (DA), and the results showed that morphometric parameters, LULC analysis, and soil loss are accepted criteria for prioritization. These results are intended to help decision-makers to prepare reliable soil erosion management plans.

Prioritization of Promising Wadi Wala Watershed (Southern Jordan) Using GIS-Based Morphometric Analysis and Multivariate Statistics

GIS-based morphometric analysis was employed for prioritization of the Wadi Wala catchment, southern Jordan. Twenty three fourth-order sub-basins were prioritized based on morphometric analysis, then appropriate soil and water conservation measures were proposed. Digital Elevation Model (DEM) and Arc GIS were used to delineate watersheds and to extract the drainage networks, and other required thematic maps (elevation and slope). LANDSAT data was used to prepare land use/land cover map, and a soil map was digitized using Arc GIS software. Linear and shape parameters were computed to prioritize 23 sub-watersheds, and ranks were designated based on the calculated compound parameter (Cp). Sub-basins grouped under a high priority class are exposed to high erosion risk;thus, they are of high potential for applying soil and water conservation measures. The current study substantiates the capability of morphometric analysis method, and geospatial technology in watershed prioritization. The Discriminant Analysis (DA) employed validates the priority classes (high, moderate, and low priority) achieved based on morphometric analysis, where they found statistically distinct from each other. Thus, it can be concluded that prioritization based only on morphometric analysis method is consistent, reliable, and of high capacity using GIS platform. Priority map along with soil, land user/cover, and slope information will help decision makers to execute proper soil and water conservation programs in the rainfed highlands of Jordan.

Morphometric and Change Detection Analysis for Prioritization of Sub Basin Conservation, Case Study of Taita Hills

The study is aimed at analyzing the risk of Taita Hills region of harmful runoff and soil erosion by employing morphometric analysis and change detection in a GIS environment to prioritize the Taita Hills in Taita Taveta County. The objective of the study was to characterize and give hierarchy in which the region should be conserved. The methodology adopted hydrological modeling, morphometric computation, Weighted Sum Analysis (WSA) and change detection. Hydrological modeling was vital in delineating the sub-watersheds and stream network. Morphometric computation and WSA was applicable in coming up with parameters and weighting the parameters for each sub-watershed’s prioritization. Change detection is related to how human activity is important for conservation as the effect of land forms and dimensions are compounded. Twenty-one fourth order streamed sub-watersheds were generated and prioritized using morphometry and change detection. Every sub-watershed is given a hierarchy based on the calculated compound parameter from the WSA equation developed and shows the risk of runoff and soil erosion. The morphometric prioritization shows 47% of the watersheds are in the high and very highly susceptible areas and there are two sub-watersheds with the highest land cover change. As well six sub-watersheds are risky with both land cover change and morphometry.

How to Support Communication among Stakeholders during Software Requirements Prioritization

Existing prioritization techniques do not support communication among stakeholders and this makes it difficult for stakeholders to understand the meaning and essence of requirements before prioritization commences. When this happens, the ordered list of requirements can be misleading. The aim of this research is to develop a method capable of supporting and computing ranks of requirements based on the criteria defined for each requirement. The proposed method is developed based on fuzzy logic. Results show that ordered requirements reproduced ranks with strong correlations when compared to their linguistic values provided by the stakeholders. The contribution of this paper centers on an improved way of prioritizing requirements with understanding.

Prioritization of Sub-Watersheds in Ruparel Watershed, Rajasthan Based on Morphometric and Land Use/Land Cover Analysis Using Remote Sensing and GIS

Watershed prioritization is considered as the most significant aspect in watershed resource management and development program. The present work attempts to prioritize seventeen sub-watersheds in Ruparel watershed of Alwar district of Rajasthan, India. For prioritization of sub-watersheds, morphometric and land use/land cover (LULC) analysis were performed using remote sensing and GIS. Base map of the study area has been derived from SOI toposheet on 1:50,000 scale whereas LULC mapping was done using IRS P6 LISS III data. Standard methods for drainage morphometry have been followed for computing morphometric parameters such as linear and shape for seventeen sub-watersheds and allotted ranks based on their relationship with erodibility and a compound value has been calculated for final ranking. Five main LULC categories were computed and were assigned priority ranks and subsequently a compound parameter was determined for final ranking. Integration of both morphometric and LULC results reveal that SBW5, SBW7, SBW12 and SBW16 are the common sub-watersheds that fall under high priority, SBW3 falls under Medium category and SBW11 comes under low priority. The results of the analysis can be used to identify the sub-watersheds which need immediate restoration and will eventually help in watershed resource management for sustainable development.