The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process

Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes. Recent empirical studies have used open source systems. These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects. Whether software metrics can still predict class error proneness in a system’s post-release evolution is still a question to be answered. This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release. Furthermore, we found that the prediction cannot be used to build a metrics model to identify error-prone classes with acceptable accuracy. These findings suggest that as a system evolves, the use of some commonly used metrics to identify which classes are more prone to errors becomes increasingly difficult and we should seek alternative methods (to the metric-prediction models) to locate error-prone classes if we want high accuracy.     

Intelligent system for visual web content analytics: A new approach and case study

The accuracy of searches for visual data elements, as well as other types of information, depends on the terms used by the user in the input query to retrieve the relevant results and to reduce the irrelevant ones. Most of the results that are returned are relevant to the query terms, but not to their meaning. For example, certain types of web contents hold hidden information that traditional search engines are unable to retrieve. Searching for the mathematical construct of 1/x using Google will not result in the retrieval of the documents that contain the mathematically equivalent expressions (i.e. x^?1). Because conventional search engines fall short of providing math-search capabilities. One of these capabilities is the ability of these search engines to detect the mathematical equivalence between users’ quires and math contents. In addition, users sometimes need to use slang terms, either to retrieve slang-based visual data (e.g. social media content) or because they do not know how to write using classical form. To solve such a problem, this paper proposed an AI-based system for analysing multilingual slang web contents so as to allow a user to retrieve web slang contents that are relevant to the user’s query. The proposed system presents an approach for visual data analytics, and it also enables users to analyse hundreds of potential search results/web pages by starting an informed friendly dialogue and presenting innovative answers.     

An Internet and wireless networking‐based water pipes web application for fault reporting

With the advancement of modern technologies such as smart cities, IoT, and e‐governments, people have to participate in their well‐being self‐governing and safe guard their key resources and critical infrastructures. To employ their efforts efficiently, many modern handy applications are created and solutions are proposed. Supported by the Ministry of Water, we designed and developed a free‐of‐charge web application (App.), easy to download from Google play store and operate using simple gadgets. Users can report water and sewage accidents fast and easy and at no cost. By merging Surveying and Geomatics Engineering with Information and Communication Technology (ICT) with Wireless communication, networking technology, and systems, we are able to create a web application to report complaints of accidents directly to the right delegated call centers at the Water Authority using the Internet and a smart mobile phone (simple gadgets). This will automate the complaint process and cancel the old paper work system. Surveying and Geomatics Engineering are concerned with the creation of an accurate GIS to manage the Water Authority data and in the data uploading into Google Maps. The developed computer program (application) works in two ways: first, it connects users (customer or employee) to the water authority electronically, and second, it allows immediate checking about the location of the complaint, momentarily filling using the Authority coordinate system, immediate action delegation (repair) at the water authority, continuous follow up, and confirmation to the user about the case. It performs as well, the needed coordinate transformation to guarantee exact filing and locations. Several software tools and programming languages are used to achieve this work such as ArcGIS, Google earth, Google map, GIS cloud, and Java and C++. As a case study, we present the App. performance for a town “Al‐Fuheis, Jordan” using the Water Authority data for creating the needed Water pipes and Sewage (GIS). Moreover, we presented the performance and the efficiency of the App. even when using Goggle Maps data only without GIS data intervention and it proved to be satisfying as well. As a result. a web App. called “Water Pipes” for the Water Authority is posted on their web to allow users participation in the reporting and the follow up process to better conserve the very scarce water in the country. In addition, it automates the work at the Water Authority from filing to take action and repair electronically in no time.     

Experimental Study of Heat Transfer Enhancement in Solar Tower Receiver Using Internal Fins

The receiver is an important element in solar energy plants. The principal receiver’s tubes in power plants are devised to work under extremely severe conditions, including excessive heat fluxes. Half of the tube’s circumference is heated whilst the other half is insulated. This study aims to improve the heat transfer process and reinforce the tubes’ structure by designing a new receiver; by including longitudinal fins of triangular, circular and square shapes. The research is conducted experimentally using Reynolds numbers ranging from 28,000 to 78,000. Triangular fins have demonstrated the best improvement for heat transfer. For Reynolds number value near 43,000 Nusselt number (Nu) is higher by 3.5% and 7.5%, sequentially, compared to circular and square tube fins, but varies up to 6.5% near Re = 61000. The lowest friction factor is seen in a triangular fin receiver; where it deviates from circular fins by 4.6%, and square fin tubes by 3.2%. Adding fins makes the temperature decrease gradually, and in the case of no fins, the temperature gradient between the hot tube and water drops sharply in the planed tube by 7%.     

Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling

The objective of the present study is to monitor and predict the changes in land surface temperature (LST) in the North of Jordan during the Period 2000 to 2016. Due to political instability in the nearby countries Syria and Iraq, Jordan has witnessed increased influxes of refugees, starting from the year 2003, which has been led to the urban expansion in the area that reflected on the climatic conditions and affected the LST values. Satellite images were used for providing LST, the acquired images represented two seasons of each year, namely summer and winter. Simulation and prediction of LST values for the next 10 years were carried out using nonlinear autoregressive exogenous (NARX) artificial neural network (ANN) model. The inputs to the model consist of meteorological data collected from eight stations in the study area, population, and land use and land cover (LULC). In fact, LULC was expressed in terms of normalized difference building index (NDBI) and normalized difference vegetation index (NDVI) that were obtained from satellite images. The model showed a high correlation between these parameters and the values of simulated LST, where the correlation coefficient for the training set, validation set, testing set and for the entire data ranged from 0.91 to 0.92. Based on the predicted LST values, LST maps for the next 10 years were developed and compared with the present actual LST maps for the year 2016. The comparison has shown an average increase of 1.1 ^°C in the average LST values, which is considered a significant increase compared with previous studies.     

Correction to: On the relationship between similar requirements and similar software

Requirements Engineering -     

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.

     

Estimating Subgrade Reaction Modulus for Transversely Isotropic Rock Medium

Most of the rock medium possesses intrinsic grain orientation or preferred bedding and joint directions, thus requiring the use of at least transverse isotropy to describe its elastic behavior. This paper presents a series of charts, based on extensive finite element parametric studies along with nonlinear regression analysis of FE simulation results, for estimating the subgrade reaction modulus (or initial tangent to the p-y curve) using five elastic constants of a transversely isotropic rock mass. The proper characterization of subgrade reaction modulus is critical for accurate prediction of the elastic lateral deflection of a rock socketed drilled shaft under the applied lateral loads. The sensitivity of the response of a laterally loaded drilled shaft to the degree of anisotropy and orientation of the plane of anisotropy (bedding plane direction of the rock medium) was demonstrated in this paper for an actual lateral load testing case in Ohio. It is highly recommended to use five elastic constants to estimate subgrade reaction modulus of rock medium exhibiting high degree of cross anisotropy.