Critical success factors for software projects: a comparative study

Although there have been studies completed on the critical success factors of software projects, these studies all have been specific to one particular country. There has been no comprehensive study reporting on different project sizes in various domains and in multiple countries. We present our extensive literature survey of critical success factors that impact software projects. Forty-three articles from the years 1990 to 2010 were found to be significant contributions that could be analysed in order to develop a list of critical factors that specifically affect the success of software projects. The method of content analysis and frequency analysis was adopted. Twenty-six critical success factors were found to be related to software project success. We suggest that organisation or project manager is attentive to control the top five critical factors to drive towards project success since the percentage of frequency of occurrences for each is more than 50%. Also, it appears that non-technical factors (94%) dominated over technical factors (6%). In a result unique to our study compared with previous one, we found that the factors of clear and frozen requirements, realistic estimation of the schedule and budget, along with a competent project manager are the five most critical success factors of software projects

Social Media Engagement Metric Analysis - "Study on Fan Page Content"

Social Media is now determined as an excellent communicative tool to connect directly with consumers. One of the most significant ways to connect with the consumers through these Social Networking Sites (SNS) is to create a facebook fanpage with brand contents and to place different posts periodically on these fanpages. In measuring social networking sites’ effectiveness, corporate houses are now analyzing metrics in terms of calculating engagement rate, number of comments/share and likings in fanpages. So now, it is very important for the marketers to know the effectiveness of different contents or posts of fanpages in order to increase the fan responsiveness and engagement rate in the fan pages. In the study the authors have analyzed total 1834 brand posts from 17 international brands of Electronics companies. Data of 9 months (From December 2014 to August 2015) have been collected for analyses, which were available online in the Brand’ fan pages. An econometrics analysis is conducted using Eviews 9, to determine the impact of different contents on fanpage engagement. The study picked the four most frequently posted content to determine their impact on PTA (people Talking About) metrics and Fanpage engagement activitie

The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches

The detection of software vulnerability requires critical attention during the development phase to make it secure and less vulnerable. Vulnerable software always invites hackers to perform malicious activities and disrupt the operation of the software, which leads to millions in financial losses to software companies. In order to reduce the losses, there are many reliable and effective vulnerability detection systems introduced by security communities aiming to detect the software vulnerabilities as early as in the development or testing phases. To summarise the software vulnerability detection system, existing surveys discussed the conventional and data mining approaches. These approaches are widely used and mostly consist of traditional detection techniques. However, they lack discussion on the newly trending machine learning approaches, such as supervised learning and deep learning techniques. Furthermore, existing studies fail to discuss the growing research interest in the software vulnerability detection community throughout the years. With more discussion on this, we can predict and focus on what are the research problems in software vulnerability detection that need to be urgently addressed. Aiming to reduce these gaps, this paper presents the research interests’ taxonomy in software vulnerability detection, such as methods, detection, features, code and dataset. The research interest categories exhibit current trends in software vulnerability detection. The analysis shows that there is considerable interest in addressing methods and detection problems, while only a few are interested in code and dataset problems. This indicates that there is still much work to be done in terms of code and dataset problems in the future. Furthermore, this paper extends the machine learning approaches taxonomy, which is used to detect the software vulnerabilities, like supervised learning, semi-supervised learning, ensemble learning and deep learning. Based on the analysis, supervised learning and deep learning approaches are trending in the software vulnerability detection community as these techniques are able to detect vulnerabilities such as buffer overflow, SQL injection and cross-site scripting effectively with a significant detection performance, up to 95% of F1 score. Finally, this paper concludes with several discussions on potential future work in software vulnerability detection in terms of datasets, multi-vulnerabilities detection, transfer learning and real-world applications

Addressing a critical success factor for software projects: a multi-round Delphi study of TSP

A five-round Delphi study was conducted to determine the degree to which the Team Software Process (TSP) can address the identified critical success factors for software projects. Three high-reputation and high-calibre experts participated in this study. Our results found agreement among the experts that the TSP provided the 'Best Practises' for addressing 14 critical success factors. The experts also agreed that the TSP provided a 'Very Good' framework for addressing 4 critical success factors. Our findings further suggested that 6 critical success factors were addressed by the TSP at a 'Good' level; only 1 critical success factor was addressed to a limited degree and none of the critical success factors were addressed at the 'Fair' level. The only critical success factor not addressed by the TSP was the 'good performance by vendors/contractors/consultants' factor. From an expert's perspective, the TSP provides an operational framework that addresses 21 critical success factors software projects. We believe that each framework or method on its own cannot address all the identified critical success factors. However, by combining a software process improvement and project management framework or other excellent software development process models, all of the critical success factors can be better addressed

Social media content analysis "Study on brand posts of electronics companies"

Social media is considered as a first-rate open communication platform to connect directly with customers. One of the most noteworthy customs to connect with the consumers through these Social Networking Sites (SNS) is to create a Facebook fanpage with brand contents and to place different posts periodically on these fanpages. According to different posts or contents placed on the fanpages, consumer responses in different manners. Usually users click like button on particular brand fanpages and then put like, comments or keep sharing on particular posts of fanpages. These types of consumer activities in fanpages reflect brand's post interactivity and engagement. Most importantly, in measuring social networking site's effectiveness, corporate houses are now analyzing metrics interms of calculating engagement rate, number of comments/share and likings infanpages. So now, it is very vital for the e-marketers to identify the effectiveness of different contents or posts of fanpages in order to increase the fan engagement rate in the fan pages. In the study the researchers have used the Netnographics technique and analyzed total 1325 brand posts from 17 international brands of electronics companies. Data of 8 month (From September 2015, April 2016) have been collected for analyses which were available online from Brand fan pages. Finally, the researchers analyzed the descriptive statistic; (Post frequency, post engagement metrics) of different posts in each Brand fanpages. The study elaborated the types of post in fanpages and their implication on generating user's engagement activities. Findings discovered two types of post (image with details and feature video post) most effective in producing user engagement. The descriptive statistics will guide the e-marketers of electronics companies to have a comprehensive idea on social media content strategy

Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques

This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers’ reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices

Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method

This study aimed to identify the relative importance of the Critical Success Factors (CSFs) that contribute to the success and development of hotel industry in Malaysia. Drawing uponTechnology-Organization-Environment (TOE) and Human-Organization-Technology (HOT) fit models, this study developed a theoretical model to predict the significant factors that contribute to the success and development of hotel industry. A two-stage Structural Equation Modelling (SEM)-Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method was applied to test the research model and reveal the importance level of factors in the model. SEM analysis showed that the use of IT, financial performance, benchmarking, service standardization, top management support, customer satisfaction, service quality, hotel interior and exterior design/look, location, employee training and empowerment were significant factors influencing hotel success and development. Moreover, the results of ANFIS showed that customer satisfaction under human dimension was the most critical predictor of hotel success and development

Cultural Issues in Offshore Teams: A Categorization based on Existing Studies

Cultural and personal issues resulting from dispersed teams are considered to be serious barriers to form trust and organize effective agile teams. However, apart from separate, reported evidence of such issues from work experience, there has been no theoretical classification in literature. This paper provides a list and analysis of common challenges mainly resulting from cultural differences and barriers in Agile Software Development (ASD) offshore teams. The data source comprise Articles published in IEEE, mostly of conferences related to ASD. Among the Articles, papers with concrete evidence of Agile Methods (AM) implementation were selected. The results show that despite the relative significance of such issues, ASD adopters typically still rely on their own experience, and creativity rather than using well-defined methods. Moreover, this study reveals that the notion of trust, as discussed in the literature, mainly refers to maintaining the pace of communication, which is the focal point in ASD. © 2019 KSII