Building Software Research Articles

Environmental and Economic Assessment of Eco-Concrete for Residential Buildings: A Case Study of Santiago de Cali (Colombia)

Although the circular economy principles date back to the late 1960s, only with the recent stimulus from the European Commission and the Ellen McArthur Foundation has this concept gained attention worldwide. The City Hall of Santiago de Cali (Colombia) is implementing a circular economy model through a sustainable construction handbook and its certification. Among others, these stimulate the use of eco-concrete using fly ash and blast furnace slag coming from local industries (industrial symbiosis). Although concretes with these supplementary cementitious materials have been widely investigated regarding mechanical and durability properties, the economic and environmental impacts have been scarcely and independently evaluated, making the material selection a complex process. Therefore, this article presents the environmental and economic assessment of eco-concretes using fly ash and blast furnace slag for the design of a house located in Santiago de Cali (Colombia). The environmental and economic impacts are estimated by means of the environmental life cycle assessment (LCA) and life cycle costing (LCC), which are methodologies based on the ISO and ASTM standards implemented in the online software Building for Environmental and Economic Sustainability (BEES), which was selected for this case study. The results indicate that 40% fly ash concrete or 50% blast furnace slag would be recommended for reducing acidification or global warming potential, respectively. However, considering the existing public policies, the best option for the case study is 50% slag concrete. These results are of significant importance as they allow providing data-based recommendations for designers during the selection of the different eco-concretes. Additionally, these results might help establish a national roadmap to reduce carbon dioxide emissions from the construction sector, which are projected to continue increasing until 2050.

Durable 3D murine ex vivo retina glaucoma models for optical coherence tomography.

Durable and standardized phantoms with optical properties similar to native healthy and disease-like biological tissues are essential tools for the development, performance testing, calibration and comparison of label-free high-resolution optical coherence tomography (HR-OCT) systems. Available phantoms are based on artificial materials and reflect thus only partially ocular properties. To address this limitation, we have performed investigations on the establishment of durable tissue phantoms from ex vivo mouse retina for enhanced reproduction of in vivo structure and complexity. In a proof-of-concept study, we explored the establishment of durable 3D models from dissected mouse eyes that reproduce the properties of normal retina structures and tissue with glaucoma-like layer thickness alterations. We explored different sectioning and preparation procedures for embedding normal and N-methyl-D-aspartate (NMDA)-treated mouse retina in transparent gel matrices and epoxy resins, to generate durable three-dimensional tissue models. Sample quality and reproducibility were quantified by thickness determination of the generated layered structures utilizing computer-assisted segmentation of OCT B-scans that were acquired with a commercial HR-OCT system at a central wavelength of 905 nm and analyzed with custom build software. Our results show that the generated 3D models feature thin biological layers close to current OCT resolution limits and glaucoma-like tissue alterations that are suitable for reliable HR-OCT performance characterization. The comparison of data from resin-embedded tissue with native murine retina in gels demonstrates that by utilization of appropriate preparation protocols, highly stable samples with layered structures equivalent to native tissues can be fabricated. The experimental data demonstrate our concept as a promising approach toward the fabrication of durable biological 3D models suitable for high-resolution OCT system performance characterization supporting the development of optimized instruments for ophthalmology applications.

Modular models for systems based on multi-tenant services: A multi-level petri-net-based approach

Recently, the emergence of multi-tenant services-based systems (SBS) in cloud has gained significant attention. These systems, commonly adopted by Cloud software vendors, offer the advantage of building software as a service (SaaS) in a multi-tenant environment with distinct variability plans. However, the task of managing the SBS is challenging due to the interdependence among its diverse components. The difficulty primarily stems from the specific performance requirements of different variability plans and the interdependence between these plans. The main concerns involve effectively representing the components and levels of this system, and establishing synchronization rules to ensure its seamless operation. Refining these rules is crucial for improving coordination among the various components and levels of the SBS. However, it becomes particularly challenging when there are correlation issue among services that fall under different plan characteristics. In this context, it is vital to guarantee performance differentiation for each tenant by adhering to predefined quality of service (QoS) specified in Service Level Agreements (SLAs).To address these challenges, we propose a modular modeling approach based on multi-level Petri-Nets (n-NLS). By adopting these approach, the ambiguity of SBS system can be significantly reduced. An example of a SBS application illustrates our modular modeling approach using n-NLS.

E-APK: Energy pattern detection in decompiled android applications

Energy efficiency is a non-functional requirement that developers must consider, particularly when building software for battery-operated devices like mobile ones: a long-lasting battery is an essential requirement for an enjoyable user experience. In previous studies, it has been shown that many mobile applications include inefficiencies that cause battery to be drained faster than necessary. Some of these inefficiencies result from software patterns that have been catalogued, and for which more energy-efficient alternatives are also known. The existing catalogues, however, assume as a fundamental requirement that one has access to the source code of an application in order to be able to analyse it. This requirement makes independent energy analysis challenging, or even impossible, e.g. for a mobile user or, most significantly, an App Store trying to provide information on how efficient an application being submitted for publication is. We study the viability of looking for known energy patterns in applications by decompiling them and analysing the resulting code. For this, we decompiled and analysed 420 open-source applications by extending an existing tool, which is now capable of transparently decompiling and analysing android applications. With the collected data, we performed a comparative study of the presence of four energy patterns between the source code and the decompiled code. We performed two types of analysis: (i) comparing the total number of energy pattern detections; (ii) comparing the similarity between energy pattern detections. When comparing the total number of detections in source code against decompiled code, we found that 79.29% of the applications reported the same number of energy pattern detections. To test the similarity between source code and APKs, we calculated, for each application, a similarity score based on our four implemented detectors. Of all applications, 35.76% achieved a perfect similarity score of 4, and 89.40% got a score of 3 or more out of 4. Furthermore, only two applications got a score of 0. When viewed in tandem, the results of the two analyses we performed point in a promising direction. They provide initial evidence that static analysis techniques, typically used in source code, can be a viable method to inspect APKs when access to source code is restricted, and further research in this area is worthwhile.

Security Monitoring during Software Development: An Industrial Case Study

The devastating consequences of successful security breaches that have been observed recently have forced more and more software development enterprises to shift their focus towards building software products that are highly secure (i.e., vulnerability-free) from the ground up. In order to produce secure software applications, appropriate mechanisms are required for enabling project managers and developers to monitor the security level of their products during their development and identify and eliminate vulnerabilities prior to their release. A large number of such mechanisms have been proposed in the literature over the years, but limited attempts with respect to their industrial applicability, relevance, and practicality can be found. To this end, in the present paper, we demonstrate an integrated security platform, the VM4SEC platform, which exhibits cutting-edge solutions for software security monitoring and optimization, based on static and textual source code analysis. The platform was built in a way to satisfy the actual security needs of a real software development company. For this purpose, an industrial case study was conducted in order to identify the current security state of the company and its security needs in order for the employed security mechanisms to be adapted to the specific needs of the company. Based on this analysis, the overall architecture of the platform and the parameters of the selected models and mechanisms were properly defined and demonstrated in the present paper. The purpose of this paper is to showcase how cutting-edge security monitoring and optimization mechanisms can be adapted to the needs of a dedicated company and to be used as a blueprint for constructing similar security monitoring platforms and pipelines.

Python code smells detection using conventional machine learning models.

Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code smell detection using Java programming language code smell datasets. This article proposes a Python code smell dataset for Large Class and Long Method code smells. The built dataset contains 1,000 samples for each code smell, with 18 features extracted from the source code. Furthermore, we investigated the detection performance of six machine learning models as baselines in Python code smells detection. The baselines were evaluated based on Accuracy and Matthews correlation coefficient (MCC) measures. Results indicate the superiority of Random Forest ensemble in Python Large Class code smell detection by achieving the highest detection performance of 0.77 MCC rate, while decision tree was the best performing model in Python Long Method code smell detection by achieving the highest MCC Rate of 0.89.

Requirements engineering framework for human-centered artificial intelligence software systems

Context:Artificial intelligence (AI) components used in building software solutions have substantially increased in recent years. However, many of these solutions focus on technical aspects and ignore critical human-centered aspects. Objective:Including human-centered aspects during requirements engineering (RE) when building AI-based software can help achieve more responsible, unbiased, and inclusive AI-based software solutions. Method:In this paper, we present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software. We provide a catalog to elicit these requirements and a conceptual model to present them visually. Results:The framework is applied to a case study to elicit and model requirements for enhancing the quality of 360° videos intended for virtual reality (VR) users. Conclusion:We found that our proposed approach helped the project team fully understand the human-centered needs of the project to deliver. Furthermore, the framework helped to understand what requirements need to be captured at the initial stages against later stages in the engineering process of AI-based software.

Perancangan Game Edukasi Matematika Menggunakan Construct 2 Untuk Kelas VII di SMP N 1 Lareh Sago Halaban

This research is based on the problems found based on the results of interviews with mathematics teachers and students at SMPN 1 Lareh Sago Halaban that the media used in the learning process only uses the blackboard with lecture and discussion methods, there is no technology-based learning media, especially educational games that are used by teachers to students related to mathematics lessons caused a lack of interest and activeness of students in mathematics subjects. Based on this, the solution given is to design a learning media. The purpose of this research is to produce learning media products in the form of educational games with the help of software Building 2 mathematics subjects at SMPN 1 Lareh Sago Halaban. The design of mathematics education games uses the construct 2 design application at SMPN 1 Lareh Sago Halaban with the Research and Development (R&D) research method using the MDLC (Multimedia Development Life Cycle) development model from Luther Sutopo the development model is explained in 6 stages, namely Concept, Design, Materials Collection, Assembly, Testing and Distribution. The product test used in the research is the validity test, practicality test, and effectiveness test. The product test results show that the educational games designed are valid, practical and effective. For the results of the validity test of three validators, the score was 0.88, the practical test by 3 practi- tators was 0.97, and the effectiveness test of twenty people was 0.92.

COMPARATIVE ANALYSIS OF AGGREGATION AND INHERITANCE STRATEGIES IN INCREMENTAL PROGRAM DEVELOPMENT

Programming computers has been a herculean task for most programmers especially when codes grow into complex and larger software systems with multiple subprograms. Object Oriented Programming (OOP) has reduced the difficulty in the development of elegant and scalable software by presenting robust concepts such as composition, inheritance and aggregation. All these concepts have enormous assistance to the software developer in code reuse. Also these techniques can be used to build applications which can be delivered to customers in a record time. In this research a critical study, review and implementation of software building and enhancement using aggregation and inheritance. A module is built with attributes defining it properties and methods its characteristics. Incrementally more modules were added to the previous modules using either aggregation or inheritance technique. This incremental approach has proven tremendous success in software development. This as buttressed by many software development theories have shown that: software is built not manufactured; software is a collection of programs with functions and attributes based on its enhancement, also incremental software development which involves building systems from sub-systems gives a better understanding of software development process. Also the process of writing bug-free programs can be achieved with lesser difficulty which can be achieved when programs are built using modular or incremental software development approach, which employs mostly aggregation while moderately using inheritance only if all the properties and methods of those modules are needed wholesomely in classes. The result from the research will help programmers to enhance codes with much mastery.

Mixed Signals: Analyzing Software Attribution Challenges in the Android Ecosystem

The ability to identify the author responsible for a given software object is critical for many research studies and for enhancing software transparency and accountability. However, as opposed to other application markets like Apple's iOS App Store, attribution in the Android ecosystem is known to be hard. Prior research has leveraged market metadata and signing certificates to identify software authors without questioning the validity and accuracy of these attribution signals. However, Android application (app) authors can, either intentionally or by mistake, hide their true identity due to: (1) the lack of policy enforcement by markets to ensure the accuracy and correctness of the information disclosed by developers in their market profiles during the app release process, and (2) the use of self-signed certificates for signing apps instead of certificates issued by trusted CAs. In this paper, we perform the first empirical analysis of the availability, volatility and overall aptness of publicly available market and app metadata for author attribution in Android markets. To that end, we analyze a dataset of over 2.5 million market entries and apps extracted from five Android markets for over two years. Our results show that widely used attribution signals are often missing from market profiles and that they change over time. We also invalidate the general belief about the validity of signing certificates for author attribution. For instance, we find that apps from different authors share signing certificates due to the proliferation of app building frameworks and software factories. Finally, we introduce the concept of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attribution graph</i> and we apply it to evaluate the validity of existing attribution signals on the Google Play Store. Our results confirm that the lack of control over publicly available signals can confuse automatic attribution processes.

An investigation into software quality and security: Past research works and existing gaps

Software security is concerned with the protection of data, facilities and applications from harm that may be occasioned by malware attacks such as password sniffing, viruses and hijacking. It is a system-wide concept that takes into account both security mechanisms such as access control as well as the design for security, such as a robust design that renders software attack complicated. It may encompass building of secure software, which comprises of the designing of software to be attack-resistant, ensuring that software is error-free, and educating software developers, architects, and users about the building of secure artifacts. In this regard, insecure software negatively affects organization’s reputations with customers, partners, and investors. The goal of this paper is to investigate some of the issues that make the software insecure, as well as the approaches that have been developed to boost software quality and security. The outcomes indicate that various models, techniques, frameworks and approaches to software quality have been developed over the recent past. However, only a few of them give reliable evidence for creating secure software applications.

Newly Released Capabilities in the Distributed-Memory SuperLU Sparse Direct Solver

We present the new features available in the recent release of SuperLU_DIST , Version 8.1.1. SuperLU_DIST is a distributed-memory parallel sparse direct solver. The new features include (1) a 3D communication-avoiding algorithm framework that trades off inter-process communication for selective memory duplication, (2) multi-GPU support for both NVIDIA GPUs and AMD GPUs, and (3) mixed-precision routines that perform single-precision LU factorization and double-precision iterative refinement. Apart from the algorithm improvements, we also modernized the software build system to use CMake and Spack package installation tools to simplify the installation procedure. Throughout the article, we describe in detail the pertinent performance-sensitive parameters associated with each new algorithmic feature, show how they are exposed to the users, and give general guidance of how to set these parameters. We illustrate that the solver’s performance both in time and memory can be greatly improved after systematic tuning of the parameters, depending on the input sparse matrix and underlying hardware.

Entropy as a Measure of Consistency in Software Architecture

In building software architectures, the relations between elements in different diagrams are often overlooked. The first stage of building IT systems is the use of ontology terminology, not software terminology, in the requirements engineering process. Then, when constructing software architecture, IT architects more or less consciously however introduce elements that represent the same classifier on different diagrams with similar names. These connections are called consistency rules and are usually not attached in any way in a modeling tool, and only a significant number of them in the models increase the quality of the software architecture. It is mathematically proved that the application of consistency rules increases the information content of software architecture. Authors show that increasing readability and ordering of software architecture by means of consistency rules have their mathematical rationale. In this article, we found proof of decreasing Shannon entropy while applying consistency rules in the construction of software architecture of IT systems. Therefore, it has been shown that marking selected elements in different diagrams with these same names is, therefore, an implicit way to increase the information content of software architecture while simultaneously improving its orderliness and readability. Moreover, this increase in the quality of the software architecture can be measured by entropy, which allows for checking whether the number of consistency rules is sufficient to compare different architectures, even of different sizes, thanks to entropy normalization, and checking during the development of the software architecture, what is the improvement in its orderliness and readability.

ASSIST-IoT: A Modular Implementation of a Reference Architecture for the Next Generation Internet of Things

Next Generation Internet of Things (NGIoT) addresses the deployment of complex, novel IoT ecosystems. These ecosystems are related to different technologies and initiatives, such as 5G/6G, AI, cybersecurity, and data science. The interaction with these disciplines requires addressing complex challenges related with the implementation of flexible solutions that mix heterogeneous software and hardware, while providing high levels of customisability and manageability, creating the need for a blueprint reference architecture (RA) independent of particular existing vertical markets (e.g., energy, automotive, or smart cities). Different initiatives have partially dealt with the requirements of the architecture. However, the first complete, consolidated NGIoT RA, covering the hardware and software building blocks, and needed for the advent of NGIoT, has been designed in the ASSIST-IoT project. The ASSIST-IoT RA delivers a layered and modular design that divides the edge-cloud continuum into independent functions and cross-cutting capabilities. This contribution discusses practical aspects of implementation of the proposed architecture within the context of real-world applications. In particular, it is shown how use of cloud-native concepts (microservices and applications, containerisation, and orchestration) applied to the edge-cloud continuum IoT systems results in bringing the ASSIST-IoT concepts to reality. The description of how the design elements can be implemented in practice is presented in the context of an ecosystem, where independent software packages are deployed and run at the selected points in the hardware environment. Both implementation aspects and functionality of selected groups of virtual artefacts (micro-applications called enablers) are described, along with the hardware and software contexts in which they run.

Program Source-Code Re-Modularization Using a Discretized and Modified Sand Cat Swarm Optimization Algorithm

One of expensive stages of the software lifecycle is its maintenance. Software maintenance will be much simpler if its structural models are available. Software module clustering is thought to be a practical reverse engineering method for building software structural models from source code. The most crucial goals in software module clustering are to minimize connections between created clusters, maximize internal connections within clusters, and maximize clustering quality. It is thought that finding the best software clustering model is an NP-complete task. The key shortcomings of the earlier techniques are their low success rates, low stability, and insufficient modularization quality. In this paper, for effective clustering of software source code, a discretized sand cat swarm optimization (SCSO) algorithm has been proposed. The proposed method takes the dependency graph of the source code and generates the best clusters for it. Ten standard and real-world benchmarks were used to assess the performance of the suggested approach. The outcomes show that the quality of clustering is improved when a discretized SCSO algorithm was used to address the software module clustering issue. The suggested method beats the previous heuristic approaches in terms of modularization quality, convergence speed, and success rate.

SG-APSIC1063: Building and application of e-learning software for infection control, Cho Ray Hospital

Objectives: In the context of the COVID-19 pandemic over the past 2 years, training regarding infection and prevention control (IPC) has become essential in responding promptly to the pandemic. Many healthcare workers from Cho Ray Hospital and provincial hospitals need IPC training; however, human resources and facilities for continuous education and training are lacking. Therefore, IPC e-learning has become necessary for medical staff, and we designed IPC e-learning courses to meet healthcare workers’ needs for efficient, time and cost-saving training to ensure safety during the COVID-19 pandemic. Methods: All medical staff of Cho Ray Hospital were invited to participate in the infection control e-learning study. The software was developed based on the existing lectures from practical infection control protocols. Healthcare workers were asked to study the software and take a test on the their training. Results: We built the e-learning course of IPC for 5,000 participants as well as management software to manage lessons, member data, and test results. After implementation for 2 months in the hospital, 207 participants had taken the exam 2,234 times. Overall, 70.5% of participants were nurses and 14.9% were doctors. Moreover, 66.4% of participants passed the test the first time they took it, and 33.6% took the test a second time. After the second test, the percentage of members who passed the exam was 100%. Conclusions: Building and applying e-learning software for IPC training has brought about efficiency and quality of training, has reduced the use of human resources for training, and has decreased costs. The software application is being expanded to all hospitals in Vietnam.

A type language for distributed reactive components governed by communication protocols

Component based software engineering (CBSE) is a methodology that aims to design and build software systems by assembling together reusable and loosely coupled components. Applying CBSE in a distributed setting is appealing but challenging: distributed applications require different remote components to interact following a well-defined protocol. In this paper we consider a model for message passing component-based systems where components are assembled together with a protocol, and are reactive to messages in a flexible way. We propose a type language that allows capturing component reactive behaviour and checking its compatibility with the protocol. Moreover, we show the correspondence of component and type behaviours, which entails a progress property for components.

LCA based recommendations for the selection of eco-concretes using blast furnace slag and fly ash

The development of circular economy models for the construction sector brings important challenges and opportunities to research and particularly to make knowledge transfer, for example through public policies. Santiago de Cali, third major city of Colombia (South America), is developing a circular economy model policy, which is mainly based on waste valorisation. Among others, the model includes a family of eco-concrete products that replaces Portland cement by blast furnace slag and fly ash coming from local industries. Therefore, the aim of this research is to develop scientific based recommendations to stakeholders for the selection of different concrete products (i.e. beams, columns, slabs) using blast furnace slag and fly ash (eco-concretes). Considering that life cycle assessment (LCA) is the most employed tool for consolidating, comparing, and assessing sustainability impacts [1], the LCA of these concrete products for the design of a sustainable residential house was performed. The LCA software Building for Environmental and Economic Sustainability (BEES) developed by the National Institute of Standards and Technology (NIST) was selected for the study. Results include a discussion on the environmental impacts. Furthermore, a methodology for the selection of sustainable building materials is presented.

Institutional Repositories in the Linked Open Data Cloud

We believe that the wealth of digital humanity research resources that is available in Institutional Repositories in Southern Africa is largely over-looked because it is not machine explorable as defined by Berners-Lee (2006). We argue that extracting research from Institutional Repositories, which Berners-Lee (2006) describes as One-Star Linked Data, and then enriching this research and storing it as tuples in a triple store (RDF data store) will expose the research to a greater audience; make it machine explorable; and integrate it with a broader network of Linked Data, known as the Linked Open Data Cloud (LODC). Our approach is to utilize software building blocks licensed under Free (Libre) Open Source Software (FLOSS) licences and to create a pipeline that can potentially transform any Institutional Repository into into a triple store, which can be published on the Internet as part of the LODC. Our transformation process embraces under-resourced institutions by utilizing FLOSS com- ponents and utilizing digital resources already published on the internet in order to promote the research from One-Star Linked data to Five-Star Linked Data in Berners-Lee (2006) taxonomy of linked data.

Morphological quantification of the maxillary canine tooth in the domestic dog (Canis lupus familiaris)

Canine tooth shape is known to vary with diet and killing behavior in wild animals and the relationship between form and function is driven in part by selective pressure. However, comparative investigation of the domestic dog (Canis lupus familiaris) is of interest. How do they compare to their wild counterparts? This study sought to quantify and characterize the morphology of the canine tooth in the domestic dog, and to provide a preliminary investigation into the variance in canine tooth morphology across individual dogs of varying breeds. Three-dimensional (3D) models generated from micro-computed tomography (µ-CT) studies of 10 mature maxillary canine teeth from the domesticated dog (Canis lupus familiaris) were used to quantify key morphological features and evaluate variance among dogs. Results show that, utilizing modern imaging and model building software, the morphology of the canine tooth can be comprehensively characterized and quantified. Morphological variables such as second moment of area and section modulus (geometrical parameters related to resistance to bending), as well as aspect ratio, ridge sharpness, cusp sharpness and enamel thickness are optimized in biomechanically critical areas of the tooth crown to balance form and function. Tooth diameter, second moment of area, section modulus, cross sectional area, tooth volume and length as well as enamel thickness are highly correlated with body weight. In addition, we found preliminary evidence of morphological variance across individual dogs. Quantification of these features provide insight into the balance of form and function of the canine tooth in wild and domesticated canids. In addition, results suggest that variance between dogs exist in some morphological features and most morphological features are highly correlated with body weight.