Change Impact Analysis Research Articles

Evaluating the performance of clone detection tools in detecting cloned co-change candidates

Co-change candidates are the group of code fragments that require a change if any of these fragments experience a modification in a commit operation during software evolution. The cloned co-change candidates are a subset of the co-change candidates, and the members in this subset are clones of one another. The cloned co-change candidates are usually created by reusing existing code fragments in a software system. Detecting cloned co-change candidates is essential for clone-tracking, and studies have shown that we can use clone detection tools to find cloned co-change candidates. However, although several studies evaluate clone detection tools for their accuracy in detecting cloned fragments, we found no study that evaluates clone detection tools for detecting cloned co-change candidates. In this study, we explore the dimension of code clone research for detecting cloned co-change candidates. We compare the performance of 12 different configurations of nine promising clone detection tools in identifying cloned co-change candidates from eight open-source C and Java-based subject systems of various sizes and application domains. A ranked list and analysis of the results provides valuable insights and guidelines into selecting and configuring a clone detection tool for identifying co-change candidates and leads to a new dimension of code clone research into change impact analysis.

On the relationship between similar requirements and similar software

Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.

Analysis of Climate Change Impact on Resource Intensity and Carbon Emission in Protected Farming Systems Using Water-Energy-Food-Carbon Nexus

Analysis of Climate Change Impact on Resource Intensity and Carbon Emission in Protected Farming Systems Using Water-Energy-Food-Carbon Nexus

Impact Analysis of Conservation Structures and Climate Change on Streamflow of Thuthapuzha Watershed, Kerala

Impact Analysis of Conservation Structures and Climate Change on Streamflow of Thuthapuzha Watershed, Kerala

Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

Algorithms to analyze the impact of change on Enterprise Architecture

Currently, the organizations are constantly confronted with rapid and radical changes, making the company in turbulence that requires a transformation from a state to a target state. This turbulence needs agility on the part of companies, i.e. the ability to monitor their environment and be ready to react. Enterprise architecture needs transformation if its levels of abstraction are affected by its changes, so enterprise architecture needs to be agile to evolve over time. To measure the impact of change on the abstraction levels. This article proposes different algorithms to proceed to the change impact analysis in the different abstraction levels to be able to analyze the change impact of different projects on the elements of the enterprise architecture and to make it agile.

Quantitative Impact Analysis of Climate Change on Residents' Health Conditions with Improving Eco-Efficiency in China: A Machine Learning Perspective.

Climate change affects public health, and improving eco-efficiency means reducing the various pollutants that are the result of economic activities. This study provided empirical evidence of the quantitative impact analysis of climate change on the health conditions of residents across China due to improvements that have been made to eco-efficiency. First, the indicators that were collected present adequate graphical trends and regional differences with a priori evidence about their relationships to each other; second, the present study applied Sensitivity Evaluation with Support Vector Machines (SE-SVM) to Chinese provincial panel data, taking the Visits to Hospitals, Outpatients with Emergency Treatment, and Number of Inpatients as proxy variables for the health conditions of the residents in each area and temperature, humidity, precipitation, and sunshine as the climate change variables, simultaneously incorporating the calculated eco-efficiency with six controlling indicators; third, we compared in-sample forecasting to acquire the optimal model in order to conduct elasticity analysis. The results showed that (1) temperature, humidity, precipitation, and sunshine performed well in forecasting the health conditions of the residents and that climate change was a good forecaster for resident health conditions; (2) from the national perspective, climate change had a positive relationship with Visits to Hospitals and Outpatients with Emergency Treatment but a negative relationship with the Number of Inpatients; (3) An increase in regional eco-efficiency of 1% increase the need for Visits to Hospitals and Outpatients with Emergency Treatment by 0.2242% and 0.2688%, respectively, but decreased the Number of Inpatients by 0.6272%; (4) increasing the regional eco-efficiency did not show any positive effects for any individual region because a variety of local activities, resource endowment, and the level of medical technology available in each region played different roles. The main findings of the present study are helpful for decision makers who are trying to optimize policy formulation and implementation measures in the cross-domains of economic, environmental, and public health.

Estimates of soil erosion rates in a principal watershed in Gozo, Malta under current and future climatic conditions

The aim of the study was to estimate the current and future annual soil erosion rate in the Ramla watershed situated in Gozo, Malta. The RUSLE-GIS was applied using innovative approaches, such as the inclusion of the highest possible DEM data as well as reflecting climatic changes to the R Factor based on the latest climate projections for Gozo derived from a total of eight CMIP6 climate change models. A climate change impact analysis was carried to estimate the likely future risk of soil erosion in this local watershed for the first time. The estimated annual soil loss, as a percentage of the total area of study varies from low (85.1%), to moderate (13.3%) and high (1.6%). Within a climate change context, a reduction in the annual soil erosion rate has been estimated for the period 2080–2100, ranging from −5 ton ha−1 yr−1 under SSP126 to −6 ton ha−1 hr-1 under SSP585. The estimated decrease of annual soil loss within the high soil erosion risk category is attributed to an increased aridity of the local climate as projected by eight CMIP6 climate models. Specific hydrological sub-watersheds were identified within the study area that warrant urgent considerations. The outcomes of this study are based on the inclusion of micro-topographical information derived from high resolution 1 m LiDAR DEM. This modelling work is seen as the first study which can be used to provide some objective guidance on the minimization of potential soil losses within the Ramla watershed. Our estimated soil loss and its spatial distribution can assist in a fuller management plan for the sustainability of this watershed. These results should not be interpreted in absolute terms but as an understanding of the underlying controlling processes, the relative spatial and temporal variations, and of possible future scenarios.

Social life cycle assessment based on input-output analysis of the Portuguese pulp and paper sector

Social life cycle assessment (S-LCA) is a tool to address social impacts from a life cycle perspective. This work presents the first S-LCA of the Portuguese pulp and paper sector. It presents the social risks and the positive social impacts of the sector, which have not been extensively covered in the scientific literature. Two different approaches were used to compile the social life cycle inventory based on input-output analysis. Approach 1 was based on impact analysis of changes in added value. Approach 2 was based on structural path analysis, supporting the disaggregation by production levels (or tiers). The functional unit was the production of 1 United States Dollar in the Portuguese pulp and paper sector. In the social life cycle impact assessment, the social risk assessment was conducted using the Social Hotspots Database. The positive impacts were addressed considering indicators of employment and remuneration, namely total employment, number of employees, labor compensation, and compensation of employees. Based on Approach 1, the results of the social risks showed the Portuguese pulp and paper industry, commerce and forestry sectors, and the Spanish forestry sector as the critical sectors. Based on Approach 2, Portugal and Spain continued to be the countries having the greatest social risks. Other contributions of countries were identified at different production levels, particularly China in tier 3. In both approaches, the stakeholder category ‘worker’ was the most critical. Regarding the positive social impacts, Approach 1 showed that Portugal and Spain contribute to the highest employment level linked to the pulp and paper, commerce, and forestry sectors. Approach 2 supported the differentiation of employment and remuneration at different production levels. Tier 2 (direct suppliers) presented the highest employment level and persons engaged and the lowest remuneration level. The results showed that Approach 1 provides a faster and more aggregated assessment of social risks and positive social impacts. Furthermore, Approach 2 allowed a more detailed evaluation, providing a better understanding of the social risks and positive social impacts on the supply chain. Thus, Approach 2 is encouraged for different applications since it produces a better understanding of where social risks and positive social impacts occur, providing better guidance for corrective measures and decision-making.

Impact and trade off analysis of land use change on spatial pattern of ecosystem services in Chishui River Basin.

The motivation of this fundamental research is to reveal the response of ecosystem services to land use change and to support the sustainable development of ecology, economy, and society. Considering the fragile eco-environment, intensive man-land relationship and the world-famous production base of Moutai liquor in China, we select Chishui River Basin (CRB) as the research region. In this study, the InVEST model is used to evaluate the impact of land use change on the spatial pattern of four ecosystem services including water yield, soil retention, carbon storage, and nitrogen export. Meanwhile, the spatial differentiation characteristics, service level, and trade-off using Pearson correlation analysis of each ecosystem service from 2000 to 2020 are explored. The results show the following: (1) The forest increased, while the grassland and cultivated decreased. Especially, the developed has increased greatly from [Formula: see text] to [Formula: see text]. (2) Each ecosystem service has spatio-temporal heterogeneity. The water yield and nitrogen export decreased, while soil retention and carbon storage showed an upward trend. (3) There are great differences in the level of ecosystem services among different land use types. The soil retention and carbon storage of forest are the highest, and the water yield of developed is the highest. (4) There are trade-offs and synergies among ecosystem services, such as the trade-off between water yield and soil retention is increasing, the synergetic relationship between water yield and nitrogen export is decreased. This is important evidence that the forest plays a major role in providing ecosystem services, and the improvement of watershed ecological services generally benefits from the implementation of ecological governance projects in recent years. However, it is worth noting that the frequent changes in land use and the intensification of urbanization have also brought potential risks to the local ecology. Therefore, all our preliminary results throw light on the nature of the impact of land use change on ecology and the trade-off relationship of ecosystem services, which is conducive to scientific protection of watershed ecological environment and the formulation of reasonable land use planning policies.

An exploratory analysis of the differential climate change impact on women in India

Women are vulnerable to climate change (CC) and consequently natural disasters impact them adversely. In this backdrop, the present paper attempts to explore the differential impacts of CC on women. Though several initiatives have been undertaken to adapt to CC in India, there is still a gap in gendering such initiatives, thus calling for attempts at identifying knowledge gaps, and understanding the CC issues within the context of gender. The study further suggests that women’s greater participation in disaster risk management, mitigation and adaptation processes is likely to enhance the effectiveness of measures taken to address ill effects of CC.

The Response of Streamflow to Climate Change in Gumera Watershed, Blue Nile River Basin, Ethiopia

Change in water hydrology is caused by climate change. This study evaluates the response of stream flow to climate change. A study of the impact of climate change on streamflow and direct runoff in the Gumera watershed was carried out using the hydrologic model HEC-HMS. Climate change impact analysis was performed using a down scaled Regional Climate Model (RCM) called the Hadley Center Earth System Model (Had GEM-ES). In this model, bias correction was done for 365 days using power transformation for rainfall correction. The study showed that for the RCP2.6 scenario, streamflow decreases annually by 0.001% in the 2050’s. It decreases annually with change of 0.01% for RCP4.5. The RCP8.5 scenario reveals an annual decrease in streamflow of 0.002%. The study provides a broad perspective on probable hydrologic alterations in the region, and the results provide useful input for sustainable water resource development.

Can we trust tests to automate dependency updates? A case study of Java Projects

Developers are increasingly using services such as Dependabot to automate dependency updates. However, recent research has shown that developers perceive such services as unreliable, as they heavily rely on test coverage to detect conflicts in updates. To understand the prevalence of tests exercising dependencies, we calculate the test coverage of direct and indirect uses of dependencies in 521 well-tested Java projects. We find that tests only cover 58% of direct and 21% of transitive dependency calls. By creating 1,122,420 artificial updates with simple faults covering all dependency usages in 262 projects, we measure the effectiveness of test suites in detecting semantic faults in dependencies; we find that tests can only detect 47% of direct and 35% of indirect artificial faults on average. To increase reliability, we investigate the use of change impact analysis as a means of reducing false negatives; on average, our tool can uncover 74% of injected faults in direct dependencies and 64% for transitive dependencies, nearly two times more than test suites. We then apply our tool in 22 real-world dependency updates, where it identifies three semantically conflicting cases and three cases of unused dependencies that tests were unable to detect. Our findings indicate that the combination of static and dynamic analysis should be a requirement for future dependency updating systems.

Revealing alarming changes in spatial coverage of joint hot and wet extremes across India

Compared to any single hydroclimatic variable, joint extremes of multiple variables impact more heavily on the society and ecosystem. In this study, we developed new joint extreme indices (JEIs) using temperature and precipitation, and investigated its spatio-temporal variation with observed records across Indian mainland. Analysis shows an alarming rate of change in the spatial extent of some of the joint extreme phenomena, tending to remain above normal. For example, above normal hot nights and wet days events expands at a rate of 0.61% per year considering entire Indian mainland. If the historical trend continues at the same rate, consecutive cold and wet day events will drop below the threshold of mean value observed in the base line period (1981–2010) everywhere in the country by the end of the twenty-first century. In contrast, the entire country will be covered by hot nights and wet days events only (frequency of occurrence will cross the threshold of mean value observed in the base line period). This observation is also supported by the CMIP6 climate model outputs. It is further revealed that extremes of any single variable, i.e. either precipitation or temperature (e.g., Extreme Wet Days, Consecutive Wet Days, Hot Nights, and Cold Spell Duration Index), do not manifest such an alarming spatial expansion/contraction. This indicates that the consideration of the joint indices of hydroclimatic variables is more informative for the climate change impact analysis.

Application of fuzzy logic in river water quality modelling for analysis of industrialization and climate change impact on Sabarmati river

Abstract Rivers are critical to human life because they are strategically significant in the world, providing primary water supplies for various purposes. Rivers are the prime importance of any country as most of the cities are settled near the river. Due to developmental activities and increase in population, it will result in huge waste generation. Surface water quality is affected because of increasing urbanization and industrialization. The aim of this research is to examine the effect of climate change and industrialization on the water quality of the Sabarmati river using a mathematical model. For this study, four important towns along the lower Sabarmati River have been considered and water quality data was considered from 2005 to 2015. In this study, different water quality parameters were considered to derive a water quality model. Results show the water quality downstream after Ahmedabad city is worse compared to the other location where the Maximum WQI is 0.71 at Rasikapur and average WQI is 0.50 for the same location for the last 15 years. It has been observed that the effect of the monsoon and also by comparing time scale water quality model the role of regulations for industrialization also plays an important role in the quality of Sabarmati river.

Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis

AbstractSelection of suitable gridded precipitation data is deemed for hydroclimatic assessment and climate change impact analysis, especially in regions where long‐term reliable precipitation data is unavailable. A novel approach based on fuzzy entropy similarity analysis (FESA) is proposed to evaluate the performance of four reanalysis gridded precipitation datasets (GPDs) for Egypt, namely European Reanalysis v.5. (ERA5), TerraClimate, Global Land Data Assimilation System (GLDAS)—Noah Land Surface Model L4 v.2 and Climatologies at high resolution for the Earth's land surface areas (CHELSA), against gauge records. The proposed method was verified using conventional statistics. Besides, the relative performance of different GPDs was verified according to their response to the influence of North Atlantic Oscillation (NAO) and Mediterranean Oscillation (MO) on winter precipitation. The performance of the best reanalysis GPD was also compared with the gauge‐based global precipitation climatology centre (GPCC) dataset to show its reliability. The FESA revealed CHELSA as the best reanalysis GPD for Egypt. The performance assessment of GDPs based on conventional statistical metrics and visual presentation confirms the results obtained using FESA. CHELSA showed significant correlations with NAO (r = 0.3627) and MO (r = 0.624) like that obtained for gauge records. CHELSA also showed a better representation of precipitation in Egypt than GPCC at nearly half of the gauge locations. As CHELSA has a much higher spatial resolution than GPCC, it can be recommended as the proxy of gauge records in Egypt. The FESA can be used for performance analysis of gridded climate data by avoiding the complexities of using multiple statistical metrics.

Change impact analysis of complex product using an improved three-parameter interval grey relation model

Change impact evaluation of complex product plays an important role in controlling change cost and improving change efficiency of engineering change enterprises. In order to improve the accuracy of engineering change impact evaluation, this paper introduces three-parameter interval grey number to evaluate complex products according to the data characteristics. The linear combination of BWM and Gini coefficient method is used to improve the three-parameter interval grey number correlation model. It is applied to the impact evaluation of complex product engineering change. This paper firstly constructs a multi-stage complex network for complex product engineering change. Then the engineering change impact evaluation index system is determined. Finally, a case analysis was carried out with the permanent magnet synchronous centrifugal compressor in a large permanent magnet synchronous centrifugal unit to verify the effectiveness of the proposed method.

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

Impacts of climate change on wheat phenology and yield in Indus Basin, Pakistan

Aim of this study is to quantify the impacts of climate change on phenology and yield of winter wheat in rainfed and irrigated regions of Pakistan by using integration of two well-known crop models including STICS and APSIM with CORDEX-SA regional climate models (RCMs). A number of different adaptation strategies based on early sowing (i.e. S1:10 and S2:20 days), irrigation (I1:15% and I2:30% additional water) and a combination of sowing and irrigation adaptations were examined to recover the potential losses that would occur due to climate change. The data for the wheat phenology, biomass (t/ha) at different stages and yield (t/ha) was obtained from several experiments at national research institutes in Pakistan under both rainfed and irrigated conditions. After calibration and validation of both crop models (STICS and APSIM), the current climate data were replaced with the CORDEX-SA RCM-projections for climate change impact analysis. A significant rising and declining trends were observed in temperature and precipitation patterns, respectively, for the selected study regions. Consequently, a substantial impact of climate change on wheat phenology (anthesis stage, maturity stage, growing length), biomass (t/ha) and yield (t/ha) was observed under scenario periods for RCP4.5 and RCP8.5. Additionally, the adaptation strategies on wheat for rainfed regions showed a substantial improvement in wheat biomass and yield simulated by STICS model particularly for sowing-2 under RCP4.5. Irrigated regions showed more improvement for irrigation-2 (I2) and combination of sowing-1 + irrigation-2 (S1 + I2) using the STICS model under both RCPs. Overall, it was observed that changes in crop phenology had a stronger impact in terms of crop yield for RCP8.5 as compare to RCP4.5. This study provides a valuable understanding and way forward for the better wheat management under changes in precipitation and temperature patterns. The study also discuss in detail, the adaptation strategies to cope with potential damage, over two different irrigation zones (rainfed and irrigated) in Pakistan.

Analysis of climate change impact on the preservation of heritage elements in historic buildings with a deficient indoor microclimate in warm regions

Heritage preservation presents uncertainties in the global warming context. Both predicting the performance of environmental variables within the built environment and assessing the preservation conditions of heritage elements are today something of a challenge. Based on current monitorings, this study uses artificial intelligence to predict environmental performance in future scenarios. In addition, heritage elements are assessed according to threshold preservation values. The Chapel of the University of Seville was chosen as case study, applying both the threshold values from UNI 10829 and the performance index (PI) to assess the indoor environmental conditions. A total of 9 environmental treatment zones were identified according to temperature and humidity. To estimate the variables, the multilayer perceptron (MLP) was monitored and applied considering the time series of A2 future scenarios in each decade of the 21st century. The current MLP performances were analysed, thus showing a progressive reduction of the PI inside the church: at the end of the century, PI values oscillated between 6.6 and 6.8% in wooden objects, and between 1.8 and 2.3% in paintings. The results also showed a progressive increase in the number of hours and distance with respect to the optimal preservation values in the zones with a greater temperature and relative humidity, with cooling and dehumidification being the most effective strategies. This study represents a progress towards the most appropriate heritage preservation strategies by using an extrapolated methodology considering the climate change effect.