ABSTRACT Cyber security is an ongoing and critical concern due to persistent threats posed by threat actors, such as hackers and crackers. With the development of information and communication technologies (ICT), the widespread usage of software systems has transformed modern society in many ways but also created new issues in protecting confidential and sensitive information. The quantification of security measures can provide evidence to support decision‐making in software security, particularly when assessing the security performance of software systems. This entails understanding the key quality criteria of security metrics, which can assist in constructing security models aligned with practical requirements. To delve deeper into this subject, the current study conducted a systematic literature review (SLR) on security metrics and measures within the realm of secure software development (SSD). The study selected 61 research publications for data extraction based on the specific inclusion and exclusion criteria. The study identified 215 software security metrics and classified them into different phases of software development life cycle (SDLC). In order to evaluate the most cited metrics in each phase of SDLC, the strengths, weaknesses, opportunities, and threats (SWOT) analysis was performed. The SWOT analysis offers a structured framework enabling researchers to make more effective, well‐informed decisions and mitigate potential risks, ultimately contributing to more valuable research findings. The study's findings provide researchers guidance for exploring emerging trends and addressing existing gaps in SDLC. This study also provides software professionals with a more comprehensive understanding of security measurements, constraints, and open‐ended specific and general issues.

ProcessPAIR is a novel method and tool for automating the performance analysis in software development.Based on performance models structured by process experts and calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers.However, the current calibration method is not fully automatic, because, in the case of performance indicators that affect other indicators in a conflicting way, the process expert has to manually calibrate the optimal value in a way that balances those impacts.In this paper we propose a novel method to automate this step, taking advantage of training data sets.We demonstrate the feasibility of the method with an example related with the Code Review Rate indicator, with conflicting impacts on Productivity and Quality.

ProcessPAIR is a novel tool for helping software developers analyzing their personal performance. Based on a performance model calibrated from the anonymized performance data of many developers and the performance data submitted by an individual developer, it automatically identifies and ranks potential performance problems and their root causes for that developer. In this work we present WebProcessPAIR, which extends ProcessPAIR with the ability to recommend improvement actions to address the root causes identified, based on a crowdsourcing approach. A case study illustrates WebProcessPAIR usage.

High-maturity software development processes can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, conducting that analysis manually is challenging because of the potentially large amount of data to analyze and the effort and expertise required. In this paper, we present ProcessPAIR, a novel tool designed to help developers analyze their performance data with less effort, by automatically identifying and ranking performance problems and potential root causes, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be properly focused. The analysis is based on performance models defined manually by process experts and calibrated automatically from the performance data of many developers. We also show how ProcessPAIR was successfully applied for the Personal Software Process (PSP). A video about ProcessPAIR is available in https://youtu.be/dEk3fhhkduo.

Software development processes can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions.However, conducting that analysis manually is challenging because of the potentially large amount of data to analyze and the effort and expertise required.ProcessPAIR is a novel tool designed to help developers analyze their performance data with less effort, by automatically identifying and ranking performance problems and potential root causes.The analysis is based on performance models derived from the performance data of a large community of developers.In this paper, we present the results of an experiment conducted in the context of Personal Software Process (PSP) training, to show that ProcessPAIR is able to accurately identify and rank performance problems and potential root causes of individual developers so that subsequent manual analysis for the identification of deeper causes and improvement actions can be properly focused.

Abstract High‐maturity software development processes, such as the Team Software Process and the accompanying Personal Software Process (PSP), can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, there is a lack of tool support for automating that type of analysis, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality, and productivity, to enable the automated (tool based) analysis of performance data produced by PSP developers, namely, identify and rank performance problems and their root causes. A PSP data set referring to more than 30 000 projects was used to validate and calibrate the model. Copyright © 2015 John Wiley & Sons, Ltd.

High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Team Software Process (TSP) and the accompanying Personal Software Process (PSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, there is a lack of tool support for automating the data analysis and the recommendation of improvement actions, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality and productivity, to enable the automated (tool based) analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and subsequently recommend improvement actions. Performance ranges and dependencies in the model were calibrated and validated, respectively, based on a large PSP data set referring to more than 30,000 finished projects.