18 research outputs found

    Software development metrics prediction using time series methods

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    The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it’s declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software’s release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.Peer ReviewedPostprint (author's final draft

    Comprehensive Approach to Increase Cyber Security and Resilience

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    In this paper the initial results of the European project CAMINO in terms of the realistic roadmap to counter cyber crime and cyber terrorism are presented. The roadmap is built in accordance to so called CAMINO THOR approach, where cyber security is perceived comprehensively in 4 dimensions: Technical, Human, Organizational, and Regulatory

    Measuring and improving Agile Processes in a small-size software development company

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    Context: Agile software development has become commonplace in software development companies due to the numerous benefits it provides. However, conducting Agile projects is demanding in Small and Medium Enterprises (SMEs), because projects start and end quickly, but still have to fulfil customers' quality requirements. Objective: This paper aims at reporting a practical experience on the use of metrics related to the software development process as a means supporting SMEs in the development of software following an Agile methodology. Method: We followed Action-Research principles in a Polish small-size software development company. We developed and executed a study protocol suited to the needs of the company, using a pilot case. Results: A catalogue of Agile development process metrics practically validated in the context of a small-size software development company, adopted by the company in their Agile projects. Conclusions: Practitioners may adopt these metrics in their Agile projects, especially if working in an SME, and customise them to their own needs and tools. Academics may use the findings as a baseline for new research work, including new empirical studies.The authors would like to thank all the members of the QRapids H2020 project consortium.Peer ReviewedPostprint (published version

    Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

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    Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the "classical" crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim's machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird's eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection

    Strategies to manage quality requirements in agile software development: a multiple case study

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    Agile methods can deliver software that fulfills customer needs rapidly and continuously. Quality requirements (QRs) are important in this regard; however, detailed studies on how companies applying agile methods to manage QRs are limited, as are studies on the rationale for choosing specific QR management practices and related challenges. The aim of this study was to address why practitioners manage QRs as they do and what challenges they face. We also analyzed how existing practices mitigate some of the found challenges. Lastly, we connect the contextual elements of the companies with their practices and challenges. We conducted 36 interviews with practitioners from four companies of varying sizes. Since each company operates in different domains, comparing QR management strategies and related challenges in different contexts was possible. We found that the companies apply proactive, reactive, and interactive strategies to manage QRs. Additionally, our study revealed 40 challenges in six categories that companies applying agile methods may face in QR management. We also identified nine contextual elements that affect QR management practice choices and which, importantly, can explain many related challenges. Based on these findings, we constructed a theoretical model about the connection between context, QR management practices, and challenges. Practitioners in similar contexts can learn from the practices identified in this study. Our preliminary theoretical model can help other practitioners identify what challenges they can expect to face in QR management in different developmental contexts as well as which practices to apply to mitigate these challenges.This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 732253.Peer ReviewedPostprint (published version

    Continuously assessing and improving software quality with software analytics tools: a case study

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    In the last decade, modern data analytics technologies have enabled the creation of software analytics tools offering real-time visualization of various aspects related to software development and usage. These tools seem to be particularly attractive for companies doing agile software development. However, the information provided by the available tools is neither aggregated nor connected to higher quality goals. At the same time, assessing and improving software quality has also been a key target for the software engineering community, yielding several proposals for standards and software quality models. Integrating such quality models into software analytics tools could close the gap by providing the connection to higher quality goals. This study aims at understanding whether the integration of quality models into software analytics tools provides understandable, reliable, useful, and relevant information at the right level of detail about the quality of a process or product, and whether practitioners intend to use it. Over the course of more than one year, the four companies involved in this case study deployed such a tool to assess and improve software quality in several projects. We used standardized measurement instruments to elicit the perception of 22 practitioners regarding their use of the tool. We complemented the findings with debriefing sessions held at the companies. In addition, we discussed challenges and lessons learned with four practitioners leading the use of the tool. Quantitative and qualitative analyses provided positive results; i.e., the practitioners’ perception with regard to the tool’s understandability, reliability, usefulness, and relevance was positive. Individual statements support the statistical findings and constructive feedback can be used for future improvements. We conclude that potential for future adoption of quality models within software analytics tools definitely exists and encourage other practitioners to use the presented seven challenges and seven lessons learned and adopt them in their companies.Peer ReviewedPostprint (published version

    How to Navigate the Uncharted Waters of Cyberwar

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    Though we heard that “Cyberwar is coming!” as long ago as in 1993, scientists and tech experts were divided. Some of them firmly believed that the scenarios of sci-fi movies were soon to come true, others remained sceptical. As of 2023, with the world deeply dependent on various digital technologies, the cyberspace considered by NATO as another battlespace, and over a year after the attack of the Russian Federation on Ukraine, it has become apparent that the world has witnessed the first-ever true hybrid war. In an unprecedented manner, the attacker has combined “traditional”, conventional military strategy with digital warfare. This paper discusses a number of the “first-evers” and challenges related to the technology and cyberspace observed before or during the war in Ukraine, the lessons learnt so far, and what it means for software engineers, cybersecurity experts and other IT specialists

    Advances and Practical Applications of Deep and Shallow Machine Learning

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    Advances and Practical Applications of Deep and Shallow Machine Learnin

    Advanced services for critical infrastructures protection

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    In this paper an overview of the first results of FP7 CIPRNet project is presented. Particularly, we demonstrate CIPRNet services for critical infrastructure protection (CIP) stakeholders. The role of the proposed services is to support decisions in the CIP domain. Moreover, those services are expected to serve as the underpinnings for the European Infrastructures Simulation and Analysis Centre (EISAC) which, similarly to the US NISAC, should provide operational services on CIP, for the benefits of CI operators, stakeholders and the Public Authorities committed to CIP.JRC.G.5-Security technology assessmen

    Software development metrics prediction using time series methods

    No full text
    The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it’s declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software’s release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.Peer Reviewe
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