Rodina Ahmad

Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This study proposed OffSig-SinGAN: a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification. The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only. It is empirically evaluated on widely used public datasets; GPDSsyntheticSignature. The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and frechet inception distance (FID). Furthermore, various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate. Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods. The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Due to specific advantages, the volume of Software Development Outsourcing (SDO) is rapidly increasing. Because of challenges arising from the Requirements Engineering (RE) process, the anticipated benefits of SDO are not achieved in case of several projects. The objective of this research work is to recommend RE practices for addressing the commonly arising RE process issues in the case of SDO. For this reason, a thorough literature review has been undertaken, as well as two questionnaire surveys have been performed with skilled SDO industry practitioners. The surveys have been done by utilizing semi-supervised style and employing Convenience Sampling method. The 50 percent rule and a four-point Likert Scale have also been used to determine the advantages of RE practices for dealing with the issues. A comprehensive list of 147 RE practices has been extracted by conducting a Focus Group session. Furthermore, the 147 RE practices have been ranked by applying Numerical Assignment and Hundred Dollar Techniques during two Focus Group sessions. The detection and adaptation of RE practices aids in enhancing the SDO RE process, evading SDO failures, and achieving the associated SDO advantages.

The anticipated benefits of Software Development Outsourcing (SDO) are not achieved in case of several projects because of the issues that emanate from Requirements Engineering (RE) process. This research work presents a Requirements Engineering Practices (REP) model to cope with the customarily occurring issues of the RE process for SDO. To formulate the model, five workshops have been conducted and Root Cause Analysis has been performed by considering 43 commonly occurring SDO RE process issues, and 147 RE practices to tackle the issues. To discover the root causes for commonly transpiring issues, 5-Whys technique has been employed. The relevant RE practices that can be used to deal with the root causes, have been endorsed by applying Brainstorming technique. For the 43 frequently occurring issues, 89 root causes have been discovered. Afterwards, 124 relevant RE practices have been recommended to eradicate the root causes and hence to address the corresponding issues. Thus, REP model postulates the root causes for commonly occurring issues of the SDO RE process, maps the root causes to the best relevant RE practices to address the corresponding issues. The model has been evaluated by an expert panel and evaluation results have been analysed through Inter-Rater Reliability analysis and Analysis of Means. The REP model supports the RE process for SDO by i). evading the adoption of random and inappropriate RE practices for dealing with the common issues of the process, ii) helping to attain the expected benefits of SDO.

Artificial Intelligence (AI) has become a core feature of today’s real-world applications, making it a trending topic within the software engineering (SE) community. The rise in the availability of AI techniques encompasses the capability to make rapid, automated, impactful decisions and predictions, leading to the adoption of AI techniques in SE. With industry revolution 4.0, the role of software engineering has become critical for developing productive, efficient, and quality software. Thus, there is a major need for AI techniques to be applied to enhance and improve the critical activities within the software engineering phases. Software is developed through intelligent software engineering phases. This paper concerns a systematic mapping study that aimed to characterize the publication landscape of AI techniques in software engineering. Gaps are identified and discussed by mapping these AI techniques against the SE phases to which they contributed. Many systematic mapping review papers have been produced only for a specific AI technique or a specific SE phase or activity. Hence, to our best of knowledge within the last decade, there is no systematic mapping review that has fully explored the overall trends in AI techniques and their application to all SE phases.

Ample evidence in the literature emphasizes using socio-technical congruence (STC) to address coordination issues in distributed software development. The recent decades have shown a progressive growth in STC, resulting in an increasing number of research studies in the scientific corpora. However, no existing study has systematically analyzed and illustrated the research patterns, latest trends, and evolution in STC. This study aims to explore the knowledge structure and create evolutionary trajectories from STC publications. To achieve this aim, a scientometric analysis is performed that combined a critical literature review (CLR) of STC-related published research in the Web of Science and Scopus databases from 2000 to 2020.The scientometric analysis is conducted through four scientometric techniques: i) co-word network analysis, ii) co-author network analysis, iii) co-citation analysis, and iv) document clustering with timeline analysis. The study outcomes will help understand and visualize STC’s research status quo. CLR is objectively conducted to recognize the latest research topics, themes, and salient features of STC research in software development. A total of 306 bibliographic data are analyzed to generate study-related networks and density visualizations. The results reveal an evolution in the STC field from its conception to the recent developments of STC models and other related factors. This study primarily contributes to the literature by providing a systematic view related to STC research to assist software practitioners in identifying applications and key research areas. Moreover, the combination of scientometric analysis and CLR reveals key researchers, journals and conferences, institutions, prominent contributing countries, and six major research themes, including “community structure” and “socio-technical congruence” as the most prominent ones.

Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

Enterprise Architecture (EA) is a strategy to attain alignment between an enterprise’s business and Information Technology (IT) to increase the competitiveness of an enterprise. In EA implementation, Critical Success Factors (CSFs) refer to factors that may facilitate the successful implementation of the EA. This research presents a CSF model based on an exhaustive literature review and empirical data from practitioners to identify rationalised CSFs. EA implementation projects that do not fully utilize the CSFs may result in inappropriate directions and dissatisfied EA project stakeholders. A quantitative research methodology is applied in this study using questionnaires to evaluate the proposed model. The respondents include enterprise architects, IT managers, and IT consultants with considerable knowledge of EA concepts. Statistical analysis is used to identify the relationships between the proposed CSFs and the success of EA implementation. From the results, the proposed CSF model is significantly related to EA implementation success. The highest-ranking CSF is governance, indicating the importance of governance in the success of EA implementation. Moreover, the proposed CSF model appears to facilitate achieving the desired architecture. By understanding the CSF model at the onset of a project, practitioners can better plan and focus on the CSFs to achieve the desired results.