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For detecting and resolving the various types of malware, novel techniques are proposed, among which deep learning algorithms play a crucial role. Although there has been a lot of research on the development of DL-based mobile malware detection approaches, they were not reviewed in detail yet. This paper aims to identify, assess, and synthesize the reported articles related to the application of DL techniques for mobile malware detection. A Systematic Literature Review is performed in which we selected 40 journal articles for in-depth analysis. This SLR presents and categorizes these articles based on machine learning categories, data sources, DL algorithms, evaluation parameters & approaches, feature selection techniques, datasets, and DL implementation platforms. The study also highlights the challenges, proposed solutions, and future research directions on the use of DL in mobile malware detection. This study showed that Convolutional Neural Networks and Deep Neural Networks algorithms are the most used DL algorithms. API calls, Permissions, and System Calls are the most dominant features utilized. Keras and Tensorflow are the most popular platforms. Drebin and VirusShare are the most widely used datasets. Supervised learning and static features are the most preferred machine learning and data source categories.

As COVID-19 reached Turkey in March 2020, all universities switched to e-learning in a very short period. Computer and software engineering (CE/SE) undergraduate students studying at university campuses have switched to e-learning. This paper seeks to understand the e-learning experience of CE/SE undergraduate students. A questionnaire was created and applied to CE/SE undergraduate students in Turkish universities. The data were analyzed using quantitative and qualitative techniques. The questionnaire received 290 usable responses. The highlights from the findings include: the participants (1) used video recordings intensively for e-learning and found them useful; (2) found face-to-face lectures more beneficial compared to digital live lectures; (3) used external online resources to improve their learning performance in courses; (4) thought that the materials and methods utilized for assessment should be adapted to e-learning for a better and fair evaluation; (5) perceived significantly less instructor support and classmate interaction and collaboration in e-learning compared to on-campus education settings; (6) rated their perceived satisfaction from e-learning as 2.85, slightly under the mid-level of the 5-point Likert scale; (7) perceived instructor support, student interaction and collaboration, and student autonomy as noteworthy factors in high-quality e-learning.

Phishing attacks aim to steal confidential information using sophisticated methods, techniques, and tools such as phishing through content injection, social engineering, online social networks, and mobile applications. To avoid and mitigate the risks of these attacks, several phishing detection approaches were developed, among which deep learning algorithms provided promising results. However, the results and the corresponding lessons learned are fragmented over many different studies and there is a lack of a systematic overview of the use of deep learning algorithms in phishing detection. Hence, we performed a systematic literature review (SLR) to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications. We address nine research questions and provide an overview of how deep learning algorithms have been used for phishing detection from several aspects. In total, 43 journal articles were selected from electronic databases to derive the answers for the defined research questions. Our SLR study shows that except for one study, all the provided models applied supervised deep learning algorithms. The widely used data sources were URL-related data, third party information on the website, website content-related data, and email. The most used deep learning algorithms were deep neural networks (DNN), convolutional neural networks, and recurrent neural networks/long short-term memory networks. DNN and hybrid deep learning algorithms provided the best performance among other deep learning-based algorithms. 72% of the studies did not apply any feature selection algorithm to build the prediction model. PhishTank was the most used dataset among other datasets. While Keras and Tensorflow were the most preferred deep learning frameworks, 46% of the articles did not mention any framework. This study also highlights several challenges for phishing detection to pave the way for further research. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. The authors would like to acknowledge funding received under the Scheme for Promotion of Academic and Research Collaboration (SPARC) Scheme, Ministry of Education, Government of India. This publication is an outcome of the R&D work undertaken in the project funded under SPARC, Ministry of Education, Government of India. Scopus 2-s2.0-85130701241

Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome, time consuming and hardly capture the semantic information reported in bug reporting tools. Deep learning (DL) techniques provide practitioners with the opportunities to automatically extract and learn from more complex and high-dimensional data. Objective: The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of the utilization of DL algorithms for SDP in the literature. Method: We systematically selected a pool of 102 peer-reviewed studies and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: Main highlights include: (1) most studies applied supervised DL; (2) two third of the studies used metrics as an input to DL algorithms; (3) Convolutional Neural Network is the most frequently used DL algorithm. Conclusion: Based on our findings, we propose to (1) develop more comprehensive DL approaches that automatically capture the needed features; (2) use diverse software artifacts other than source code; (3) adopt data augmentation techniques to tackle the class imbalance problem; (4) publish replication packages., Comment: 45 pages, 12 figures

Effective and efficient data management is crucial for smart farming and precision agri-culture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture. Scopus 2-s2.0-85109414110

Technical debt in general refers to suboptimal decisions the practitioners make during software development that achieve short-term goals at the expense of long-term quality concerns. Architecture technical debt is a subset of technical debt, when software practitioners make wrong or sub-optimal decisions related to the architecture of the software. Identifying such architecture technical debt plays a crucial role in software quality. In the last decade, there were several methods proposed to identify architecture debts in the literature.In this study, we conduct a systematic literature review of methods that identify architecture technical debt by inspecting 28 primary studies published from 2011 to 2020. Based on the outcomes of our review: (1) design rule space and traceability graphs are the dominant techniques; (2) despite the increase of automated techniques in identifying architecture debt, pure manual methods using expert opinion is still popular; (3) majority of the approaches use code/version history to mine archictural technical debt; (4) the field is getting increasingly more attraction in the last five years.

Context: Knowledge level and productivity of the software engineering (SE) workforce are the subject of regular discussions among practitioners, educators, and researchers. There have been many efforts to measure and improve the knowledge gap between SE education and industrial needs. Objective: Although the existing efforts for aligning SE education and industrial needs have provided valuable insights, there is a need for analyzing the SE topics in a more “fine-grained” manner; i.e., knowing that SE university graduates should know more about requirements engineering is important, but it is more valuable to know the exact topics of requirements engineering that are most important in the industry. Method: We achieve the above objective by assessing the knowledge gaps of software engineers by designing and executing an opinion survey on levels of knowledge learned in universities versus skills needed in industry. We designed the survey by using the SE knowledge areas (KAs) from the latest version of the Software Engineering Body of Knowledge (SWEBOK v3), which classifies the SE knowledge into 12 KAs, which are themselves broken down into 67 subareas (sub-KAs) in total. Our analysis is based on (opinion) data gathered from 129 practitioners, who are mostly based in Turkey. Results: Based on our findings, we recommend that educators should include more materials on software maintenance, software configuration management, and testing in their SE curriculum. Based on the literature as well as the current trends in industry, we provide actionable suggestions to improve SE curriculum to decrease the knowledge gap.

14th Turkish National Software Engineering Symposium (UYMS) -- OCT 07-09, 2020 -- ELECTR NETWORK, In the internet advertising industry, web and mobile applications that display ads need to choose high-paying ads to increase their revenue. Ad mediators create various decision mechanisms to select ads that will generate higher revenues in order to increase the revenue of advertising applications. One type of these decision mechanisms is to select and deliver the ad with the highest eCPM (Effective Cost Per Mille) value from ads that can be placed in an ad slot. The eCPM value varies depending on different external factors for different applications. It is not possible for domain experts to make successful predictions by analyzing different sets of external factors for many applications and to keep these predictions constantly updated. Therefore, eCPM values were automatically predicted separately for each application on different ad slots and different countries using time series analysis and machine learning algorithms. SARIMA, MLP, CNN and LSTM algorithms are used to make predictions. The LSTM algorithm has generally yielded better results in eCPM estimation. As a result of the trials conducted with a limited number of users of the two applications on production environment, an increase in daily income per user was observed., Istanbul Okan Univ,IEEE Turkey Sect,Yazilim Sanayicileri Dernegi,Logo,Dalya

According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce.

Giderek daha fazla sayıda organizasyonun faaliyetleri bilgi sistemlerine bağımlı duruma gelmektedir. Bu da bilgi sistemlerini hayatımızın önemli bir bileşeni durumuna getirmektedir. Küreselleşme ve zorlaşan rekabet nedeniyle, rekabet avantajı elde etmek ya da bunu korumak için stratejik maliyet yönetimi vazgeçilmez bir konuma gelmiştir. Bu iki eğilim, bilgi sistemlerinin toplam sahip olma maliyetlerinin (TSOM) hesaplanmasını önemli kılmaktadır. Bu doğrultuda, bir organizasyonun gereksinimleri doğrultusunda genişletilebilecek bir TSOM hesaplama çerçevesi bu çalışma kapsamında sunulmuştur. Böylece mühendislerin uygun maliyetli bilgi sistemleri geliştirmelerine ve/veya mevcut sistemlerin maliyetlerini azaltmak için farklı çözümler uygulamalarına destek olunması hedeflenmiştir. Çerçevenin, uluslararası bir şirkette kullanımını anlatan ve nasıl genişletilebileceğine dair bir örnek oluşturan vaka çalışması ile geçerlilik denetimi yapılmıştır.

This paper proposes a novel method for assessing text coherence. Central to this approach is an ontology-based representation of text, which captures the level of relatedness between consecutive sentences via ontologies. Our method encompasses annotating text using ontological concepts and assessing text coherence based on relatedness measurement among these concepts. the ontology-based relatedness measurement method used in this study considers various types of relationships in ontologies and derived relationships via an inference engine for computing relatedness. We hypothesized that rich variety of relationships and inferred facts in ontologies would improve the success of text coherence assessment. Our results demonstrate that the use of ontologies yields to coherence values that have a higher correlation with human ratings.

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of software development, where algorithms are hard-coded by humans, to ML systems materialized through learning from data. Therefore, we need to revisit our ways of developing software systems and consider the particularities required by these new types of systems. Objective: The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of software engineering (SE) research for engineering ML systems. Method: I performed a systematic literature review (SLR). I systematically selected a pool of 141 studies from SE venues and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: The non-deterministic nature of ML systems complicates all SE aspects of engineering ML systems. Despite increasing interest from 2018 onwards, the results reveal that none of the SE aspects have a mature set of tools and techniques. Testing is by far the most popular area among researchers. Even for testing ML systems, engineers have only some tool prototypes and solution proposals with weak experimental proof. Many of the challenges of ML systems engineering were identified through surveys and interviews. Researchers should conduct experiments and case studies, ideally in industrial environments, to further understand these challenges and propose solutions. Conclusion: The results may benefit (1) practitioners in foreseeing the challenges of ML systems engineering; (2) researchers and academicians in identifying potential research questions; and (3) educators in designing or updating SE courses to cover ML systems engineering., Comment: accepted by the Journal of Systems and Software on 15 June 2021

Yazilim mimarisi tasarimi, yazilim gelistirme surecindeki cok onemli ve zor bir asamadir. Paydaslarin celisen amaclarini yonetmek ve ilgili gereksinimlerden mimari soyutlamalar turetmek onemlidir. Ayrica mevcut cozum alani bilgisi uzerinde temellendirilmis semantik acidan zengin ara urunler (artifact) uretmek dikkat edilmesi gereken bir husustur. Elde edilen mimari ara urunler yazilim gelistirme surecinin geri kalanina kilavuzluk eder ve planlamayi kolaylastirir. Yakin donemde yazilim gelistirme yontemlerinin ve etkinliklerinin eslenebilecegi, yazilim muhendisligine soyut ve genel bir bakis sunan Essence cercevesi onerilmistir. Bu calismada, sentez tabanli yazilim mimarisi tasarimi etkinliklerinin Essence cercevesiyle eslestirilmesi hakkinda bilgi verilmektedir. Boylece, yazilim muhendisliginin soyut ve genel bir modeli kullanilarak yazilim mimarisi tasarim etkinliklerinin aciklanmasi amaclanmistir. Ayrica, sentez tabanli yazilim mimarisi tasarim etkinlikleri icin durum tabanli etkinlik izleme duzenegi onerilmistir. Essence cercevesi ve sentez tabanli mimari tasarimi yaklasimi hakkinda elde edilen deneyim paylasilmaktadir

Context According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry. Objective Our objective is to perform a meta-analysis to aggregate the results of the studies published in this area to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps. Method To synthesize the body of knowledge, we performed a systematic literature review (SLR), in which we systematically selected a pool of 35 studies and then conducted a meta-analysis using data extracted from those studies. Results Via a meta-analysis and using data from 13 countries and over 4,000 data points, highlights of the SLR include: (1) software requirements, design, and testing are the most important skills; and (2) the greatest knowledge gaps are in configuration management, SE models and methods, SE process, design (and architecture), as well as in testing. Conclusion This paper provides implications for both educators and hiring managers by listing the most important SE skills and the knowledge gaps in the industry.

A primary concern of software development is selecting a suitable methodology to implement a software project. However, this selection is affected by many factors, with evidence suggesting that a specific set of factors defines a specific situational context for a project. This situational context leads to a project-specific software process. In this paper, we report on our analysis of a very small-sized company’s current software process based on a reference framework that identifies the factors of a situational context. The outcome of our case study confirms the earlier findings that a software process is highly dependent on situational factors. The company has a suitable situational context (such as very small-sized, experienced, skilled, cohesive team with low turnover) to apply agile practices and its software process is more close to an agile rather than plan-driven approach. Moreover, the company is continuously adopting its software process to the situational factors changing from project to project and over time.

This chapter presents an overview of system development methods (SDMs) dedicated for Internet of Things (IoT) systems. It summarizes IoT SDMs along with their process flows represented using Business Process Model and Notation. The chapter presents the characterization of these methods using the evaluation criteria. The objective of an IoT SDM is to guide a project team in developing and combining these components in order to be able to fulfill user requirements. SDM is preferred over the "software development method" concept since an IoT system encompasses many software, hardware, and communication components. An important aspect in IoT is that changes in the properties of a thing and its corresponding virtual entity need to be synchronized. The IoT Methodology is a generic, lightweight method built on iterative prototyping and Lean start-up approaches. Ignite provides guidance for developing IoT services and software components for IoT devices from a business and project management perspective; however, it does not provide a low-level, technical guidance.

Althoughseveral software product line engineering (SPLE) methods have been described inthe literature, adopting these methods in practice is often notstraightforward. Thorough understanding of the methods and their artefacts isnecessary to apply the methods in a proper manner, and likewise realize theexpected goals of SPLE. Recently the Essence framework has been proposed tomodel the essential elements of a method and to support the modeling of a broadset of software development methods including plan-driven methods and agilemethods. So far, the Essence framework has been applied to single systemdevelopment methods and not yet for SPLE methods. To enhance the understandingof SPLE methods and support a vision for tailoring SPLE methods, we provide amapping of an SPLE method to the Essence framework. We present experiencesabout modeling an SPLE method using the Essence framework within the industrialcontext of Havelsan., Literatürdebirçok yazılım ürün hattı mühendisliği (YÜHM) yöntemi tarif edilmiş olmasınarağmen bu yöntemlerin pratikte uygulanması çoğu zaman kolay olmamaktadır.YÜHM'in beklenen hedeflerini gerçekleştirmek ve yöntemleri doğru uygulamak içinhem yöntemleri hem de yapısal öğeleri tam olarak anlamak gereklidir. Essenceçerçevesi, son zamanlarda plan güdümlü yöntemler ve çevik yöntemler de dahilolmak üzere geniş bir alana yayılmış yazılım geliştirme yöntem setininmodellemesi için önerilmektedir. Şu ana kadar, sadece tekil sistem geliştirmeyöntemine uygulanan Essence çerçevesi henüz YÜHM yöntemleri içinuygulanmamıştır. Bu çalışmada, YÜHM yöntemlerinin daha iyi anlaşılmasınısağlamak ve uyarlaması konusunda bir vizyon oluşturmak için seçilen YÜHMyönteminin Essence çerçevesi ile modellenmesi gerçekleştirilmiştir. Çalışma sonucunda, Essence çerçevesi ve YÜHMhakkında elde edilmiş olan deneyim ve öğrenilmiş dersler, endüstriyel olarakHAVELSAN bağlamında, sunulmuştur.

More and more organizations are being run dependent on information systems. This makes information systems a pivotal component of our lives. Because of globalization and harsh competition, strategic cost management has become essential to keep or gain competitive advantage. These two trends make the investigation of Total Cost of Ownership (TCO) for information systems crucial. To this end, a systematic mapping study (SMS) is presented to identify the use of TCO in information systems context. A summary of the findings after analyzing and synthesizing 75 relevant publications are as follows: (1) an increased interest in TCO for information systems is observed over the years; (2) 76% of the selected publications lack validation and evaluation; (3) the main motivation behind the 72% of the publications is reduction of TCO; (4) essential means of reducing TCO are cloud computing, SaaS model, and multi-tenancy; (5) TCO calculations are also generally made to compare cloud-based infrastructures with in-house infrastructures and SaaS model with on-premise software; (6) TCO is an important criterion in making investment decisions for information systems such as ERP, CRM. Giderek daha fazla sayıda organizasyonun faaliyetleri bilgi sistemlerine bağımlı duruma gelmektedir. Bu da bilgi sistemlerini hayatımızın önemli bir bileşeni durumuna getirmektedir. Küreselleşme ve sert rekabet koşulları nedeniyle, rekabet avantajı elde etmek ya da bunu korumak için stratejik maliyet yönetimi vazgeçilmez bir konuma gelmiştir. Bu iki eğilim, bilgi sistemlerinin toplam sahip olma maliyetinin (TSOM) hesaplanmasını önemli kılmaktadır. Bu amaçla, bilgi sistemleri bağlamında TSOM kullanımını irdelemek için bir sistematik haritalama çalışması sunulmuştur. Konuyla ilgili 75 yayın analiz edildikten ve sentezlendikten sonra elde edilen bulguların bir özeti şu şekildedir: (1) bilgi sistemleri için TSOM’a yıllar boyunca artan bir ilgi gözlemlenmektedir; (2) seçilen yayınların %76’sı geçerlilik ve değerlendirme eksikliği taşımaktadır; (3) yayınların %72’sinin arkasındaki en büyük motivasyon, TSOM’un azaltılmasıdır; (4) TSOM’u azaltmanın vazgeçilmez araçları bulut bilişim, servis olarak yazılım modeli ve çoklu kiracılıktır; (5) TSOM hesaplamaları genellikle bulut tabanlı altyapıları kurum içindeki altyapılarla ve servis olarak yazılım modeli ile kurum içi yazılımları karşılaştırmak için yapılmaktadır; (6) TSOM, bilgi sistemleri için (kurumsal kaynak planlama, müşteri ilişkileri yönetimi sistemleri gibi) yatırım kararlarında önemli bir kriterdir.

Survey raw data: skills important to software professionals based on SWEBOK

The Internet of Things (IoT) is a global network of smart devices which enables these objects to collect and exchange data. Research in the IoT is still progressing, and it is now being applied in various domains. One of the key observations is that the development of IoT systems is not trivial and needs to be carefully managed to meet the required functional and quality concerns. Due to the heterogeneous aspects including software, hardware, and communication, developing the IoT systems implies various challenges that need to be explicitly considered in the development process and successfully resolved. Unfortunately, less focus has been provided so far on the development methods for the IoT systems. To address the particular IoT development concerns, we analyze and discuss the existing approaches that target the development of IoT systems. For this purpose, we use the Essence Framework, which has been recently developed as a framework for modeling various kinds of software development practices and methods. We propose an initial practice library, which can be used to develop and/or tailor project-specific IoT system development methods.

There are many methods having different approaches for assessing similarity and relatedness and they are used in many application areas, including web service discovery, invocation and composition, word sense disambiguation, information retrieval, ontology alignment and merging, document clustering, and short answer grading. These methods can be categorized as path-based, information content-based, feature-based, geometric model-based, and hybrid approaches. These approaches use resources such as concept hierarchy, conceptual graph, and corpus for computing similarity and relatedness. With the rise of the semantic web, ontologies have attracted the attention of several researchers. Ontologies represented in the Web Ontology Language (OWL) are also valuable resources for similarity and relatedness measurement. The method proposed in this paper interprets some OWL constructs to assess semantic relatedness. The motivation behind this is to benefit from the rich expressive power of OWL to obtain better semantic relatedness measurement results. The success of the method has been validated against human judgments. The correlation between human judgments and automatically computed semantic relatedness values was calculated as 0.685 and was significant at the 0.01 level.