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Abstract The software engineering community aims to achieve and maintain high‐efficient software engineering practical activities. One of the techniques used for this purpose is Test‐Driven Development (TDD), which is a cyclic and test‐centered development method positively related to maintainability, which may vary depending on the context and application. Previous research has indicated that TDD's benefits on maintainability may be due to the improved focus on coding and testing activities. However, limited research has investigated the role of engagement in software quality. Therefore, this study investigates the relationship between TDD methods, engagement level in development activities, and maintainability. The primary research questions addressed in this study are does following TDD methods improve the engagement level in development activities? And does a higher engagement level leads to better maintainability? The study employs a statistical analysis technique, regression analysis to explore the research questions. The results show that TDD behaviours improve the engagement level, and engaging in development activities has a significant positive impact on maintainability. Additionally, the study finds that the positive impact of focussing on testing, such as writing more test cases, is more pronounced compared to coding activities. Our study adds to current software engineering literature that not only personal expertise but the engagement level in the development process are associated with software quality and calls for the emphasis on developer's engagement.

Abstract As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment.

The software engineering community aims to achieve and maintain high‐efficient software engineering practical activities. One of the techniques used for this purpose is Test‐Driven Development (TDD), which is a cyclic and test‐centered development method positively related to maintainability, which may vary depending on the context and application. Previous research has indicated that TDD's benefits on maintainability may be due to the improved focus on coding and testing activities. However, limited research has investigated the role of engagement in software quality. Therefore, this study investigates the relationship between TDD methods, engagement level in development activities, and maintainability. The primary research questions addressed in this study are does following TDD methods improve the engagement level in development activities? And does a higher engagement level leads to better maintainability? The study employs a statistical analysis technique, regression analysis to explore the research questions. The results show that TDD behaviours improve the engagement level, and engaging in development activities has a significant positive impact on maintainability. Additionally, the study finds that the positive impact of focussing on testing, such as writing more test cases, is more pronounced compared to coding activities. Our study adds to current software engineering literature that not only personal expertise but the engagement level in the development process are associated with software quality and calls for the emphasis on developer's engagement. [ABSTRACT FROM AUTHOR]

Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains. [ABSTRACT FROM AUTHOR]

As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment. [ABSTRACT FROM AUTHOR]

The article sets forth and analyzes the milestones of management of software as intellectual property. Software is assessed as an asset of the company's intellectual property (IP) portfolio. Protection options for a software program are specified, and each option is considered in terms of the company's market strategy and further commercialization. Commercialization stage is placed under close scrutiny with relevant examples and reasoning. The role of a trademark in commercialization process is evaluated. [ABSTRACT FROM AUTHOR]

Around the world, the importance of Very Small Entities (VSEs), organisations having up to 25 people, has been constantly increasing. Worldwide, VSEs represent over 92% of the software industry. Therefore, two main needs can be highlighted: a) providing VSEs with resources to produce high‐quality software, and b) training software engineering undergraduates in proven practices to produce high‐quality software within the given schedule and budget. A logical way to meet these needs is transfering proven practices provided by software engineering standards. However, transferring the knowledge of software engineering standards is not always an easy task. The ISO/IEC 29110 is a series of international standards and guides that provide codified knowledge related to the software development process. The series was specifically developed to be used by VSEs. This study presents an exploratory analysis, conducted in 12 Mexican VSEs, which implemented the software Basic profile of the ISO/IEC 29110, to identify the pace at which they can adopt this standard to their environment. Besides, the benefits and difficulties encountered are provided. The results can be relevant for other VSEs interested in implementing this standard. Even if the exploratory analysis was performed in the VSEs of Mexico, this analysis can be of interest in other countries. The results obtained can help other VSEs that are interested in the adoption of this international standard to reduce the barriers to a successful implementation. [ABSTRACT FROM AUTHOR]

Great strides have been made to encourage researchers to archive data created by research and provide the necessary systems to support their storage. Additionally it is recognised that data are meaningless unless their provenance is preserved, through appropriate meta-data. Alongside this is a pressing need to ensure the quality and archiving of the software that generates data, through simulation, control of experiment or data-collection and that which analyses, modifies and draws value from raw data. In order to meet the aims of reproducibility we argue that data management alone is insufficient: it must be accompanied by good software practices, the training to facilitate it and the support of stakeholders, including appropriate recognition for software as a research output.

Feedback from software users, such as bug reports, is vital in the management of software projects. In GitHub, the feedback is typically expressed as new issues. Through filing issue reports, users may help identify and fix bugs, document software code, and enhance software quality via feature requests. In this paper, we aim at investigating some characteristics of issues to facilitate issue management and software management. We investigate the important degrees of behaviors that are related to issues in popular projects to assess the importance of issues in GitHub and analyze the effectiveness of issue labeling for issue handling. Then, we explore the patterns of issue commits over time in popular projects based on visual analysis and obtain the following results: we find that the behaviors that are related to issues play important roles in the GitHub. We also find that the time distribution of issue commits follows a three-period development model, which approximately corresponds to the project life cycle. These results may provide a new knowledge about issues that can help managers manage and allocate project resources more effectively and even reduce software failures.

The processes involved in requirements engineering are some of the most, if not the most, important steps in systems development. The need for well-defined requirements remains a critical issue for the development of any system. Describing the structure and behavior of a system could be proven vague, leading to uncertainties, restrictions, or improper functioning of the system that would be hard to fix later. In this context, this article proposes SENSE, a framework based on standardized expressions of natural language with well-defined semantics, called boilerplates, that support a flow-down procedure for requirement management. This framework integrates sets of boilerplates and proposes the most appropriate of them, depending, among other considerations, on the type of requirement and the developing system, while providing validity and completeness verification checks using the minimum consistent set of formalities and languages. SENSE is a consistent and easily understood framework that allows engineers to use formal languages and semantics rather than the traditional natural languages and machine learning techniques, optimizing the requirement development. The main aim of SENSE is to provide a complete process of the production and standardization of the requirements by using semantics, ontologies, and appropriate NLP techniques. Furthermore, SENSE performs the necessary verifications by using SPARQL (SPIN) queries to support requirement management.

Every year software development industry requires a higher number of trained software engineers who are not only skilled programmers but also talented software projects managers. To deliver high‐quality software projects, engineers require of the application of sound engineering competencies along with discipline. Obtaining those practices usually require years of experience. Companies are not prepared to invest this time on engineers resulting in a high percentage of deficient projects. Here, the authors present a bachelor‐level competency‐based approach that develops and evaluates such competencies during a challenge‐based learning experience. In this way, the rate of successful projects where software engineers are involved will be higher, as they have obtained the appropriate competencies to deliver such projects. [ABSTRACT FROM AUTHOR]

Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console.

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient. [ABSTRACT FROM AUTHOR]

Code clones are created when a developer duplicates a code fragment to reuse existing functionalities. Mitigating clones by refactoring them helps ease the long-term maintenance of large software systems. However, refactoring can introduce an additional cost. Prior work also suggest that refactoring all clones can be counterproductive since clones may live in a system for a short duration. Hence, it is beneficial to determine in advance whether a newly-introduced clone will be short-lived or long-lived to plan the most effective use of resources. In this work, we perform an empirical study on six open source Java systems to better understand the life expectancy of clones. We find that a large number of clones (i.e., 30% to 87%) lived in the systems for a short duration. Moreover, we find that although short-lived clones were changed more frequently than long-lived clones throughout their lifetime, short-lived clones were consistently changed with their siblings less often than long-lived clones. Furthermore, we build random forest classifiers in order to determine the life expectancy of a newly-introduced clone (i.e., whether a clone will be short-lived or long-lived). Our empirical results show that our random forest classifiers can determine the life expectancy of a newly-introduced clone with an average AUC of 0.63 to 0.92. We also find that the churn made to the methods containing a newly-introduced clone, the complexity and size of the methods containing the newly-introduced clone are highly influential in determining whether the newly-introduced clone will be short-lived. Furthermore, the size of a newly-introduced clone shares a positive relationship with the likelihood that the newly-introduced clone will be short-lived. Our results suggest that, to improve the efficiency of clone management efforts, practitioners can leverage our classifiers and insights in order to determine whether a newly-introduced clone will be short-lived or long-lived to plan the most effective use of their clone management resources in advance. [ABSTRACT FROM AUTHOR]

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications. [ABSTRACT FROM AUTHOR]

Monitoring and predicting the trend of bug number time series of a software system is crucial for both software project managers and software end‐users. For software managers, accurate prediction of bug number of a software system will assist them in making timely decisions, such as effort investment and resource allocation. For software end‐users, knowing possible bug number of their systems ahead will enable them to adopt timely actions in coping with the loss caused by possible system failures. This study proposes an approach called SamEn‐SVR to combine sample entropy and support vector regression (SVR) to predict software bug number using time series analysis. The basic idea is to use template vectors with the smallest complexity as input vectors for SVR classifiers to ensure predictability of time series. By using Mozilla Firefox bug data, we conduct extensive experiments to compare the proposed approach and state‐of‐the‐art techniques including auto‐regressive integrated moving average (ARIMA), X12 enhanced ARIMA and polynomial regression to predict bug number time series. Experimental results demonstrate that the proposed SamEn‐SVR approach outperforms state‐of‐the‐art techniques in bug number prediction. [ABSTRACT FROM AUTHOR]

The size of a software project is a key measure of predicting software effort at the requirements and analysis phase. Use case points (UCP) is among software size metrics that achieved good reputation because of the increasing popularity of use case driven development methodologies in software industry. Nevertheless, there is no consistent method that can effectively translate the UCP into its corresponding effort. Previous estimation models were built using a very limited number of projects, and they were not well examined. The soft computing techniques were rarely applied for such problem and their performances have not been well investigated using a systematic procedure. This study looks into the accuracy and stability of some soft computing methods for the problem of effort estimation based on UCP. Four neural network methods, adaptive neuro fuzzy inference system and support vector regression have been used in this comparative study. The results suggest that most used soft computing techniques can work well with good accuracy for such problem. Among them, the general regression neural network is the superior one with stable ranking across different accuracy measures. Also, it has been found that using adjustment variables with basic UCP variables, solely or together, have positive impact on the accuracy and stability. [ABSTRACT FROM AUTHOR]

This paper presents a new concept of an approach to risk assessment which can be done on the basis of publicly available information about vulnerabilities. The presented approach uses also the notion of trust and implements many concepts used in so called trust and reputation management systems (which are widely used in WSN, MANET or P2P networks, but also in ecommerce platforms). The article shows first outcomes obtained from the presented model. The outcomes demonstrate that the model can be implemented in real system to make software management more quantified and objective process, which can have real and beneficial impact on institutional security. In article, however the emphasis was set not on the model itself (which can be easily changed) but on the possibility of finding useful information about vulnerabilities. [ABSTRACT FROM AUTHOR]

Background Gentoo is a GNU/Linux metadistribution designed to maximize and simplify user control of the software environment. All determinants of a Gentoo environment are recorded in a small number of plain-text configuration files, from which the software make-up of the system can be reconstructed entirely. As such, Gentoo constitutes a replicable and transparent software infrastructure - as mandated by research valuing reproducibility. Of equal scientific interest is the flexibility of Gentoo's package management. All software is distributed in a rolling-release fashion, giving the user full control over which versions (including live versions and branches/tags from version control) of which programs to install, and with which compilation options. All of the above is accompanied by automatic, version-aware dependency resolution, which also tracks static library linking and prompts for rebuilds as necessary. We believe Gentoo is excellently suited to address many of the challenges in neuroscience software management; including: system replicability, system documentation, data analysis reproducibility, fine-grained dependency management, easy control over compilation options, and seamless access to cutting-edge software releases. New information We have made a substantial set of neuroimaging and data analysis packages - including their entire dependency stacks - available for any system using Gentoo's Package Management Standard. Neuroscientific software now usable under Gentoo includes but is not limited to: Dipy (Garyfallidis et al. 2014) FSL (Jenkinson et al. 2012) Nipype (Gorgolewski et al. 2016) Nilearn (Abraham et al. 2014) PsychoPy (Peirce 2008) Herein we describe the implementation and current capabilities of this environment, as well as its ability to accelerate and improve research. [ABSTRACT FROM AUTHOR]

Although traceability information is vital to many software engineering activities, agile development approaches rarely use it because of the extra effort required. In this industry case study, the authors report the results of applying their lightweight semiautomated method that integrates traceability into software architecture developed with an agile process like Scrum. [ABSTRACT FROM PUBLISHER]

The present paper is the result of the following investigation: "Design of a model to improve the cost estimation processes for software developing companies". It is presented a revision of the literature at an international level for identifying tendencies and methods for more accurate software cost estimations. Trough the Delphi predictive method, a group of experts in the field of software development in Barranquilla qualified and assessed from the probability of occurrence five realistic estimation scenarios. A completely random experiment was designed whose results point to two scenarios statistically similar in a cualitative manner, from that a three agents model was built: Metodology, capacity of the work team and technological products; each of those with three categories of fulfilment to achieve more precise estimations. [ABSTRACT FROM AUTHOR]

Thinking about improving the management of software development in software firms has been dominated by one approach: the capability maturity model (CMM) devised and administered at Carnegie Mellon University. Although widely known and used, there are a number of well-understood difficulties and limitations with this approach. This article examines, through the lens of modern management theory, the governing assumptions about management and organizational improvement behind the CMM approach. It characterizes this assumption set as the industry model of software management improvement. We take a dialectic approach to propose antithetical assumptions and a configuration approach to weave these different assumptions into alternative assumption platforms: the knowledge model and the network model. These two models, we suggest, might be better foundations for some types of software managements in a world responding to globalization and rapid technology change. If these assumption platforms were used to underpin improvements in software management, we ask, what kinds of approaches would they lead to? [ABSTRACT FROM AUTHOR]

In recent years, many companies have realized that collaboration with a thriving user or developer community is a major factor in creating innovative technology driven by market demand. As a result, businesses have sought ways to stimulate contributions from developers outside their corporate walls, and integrate external developers into their development process. To support software companies in this process, this paper presents an empirical study on the contribution management processes of two major, successful, open source software ecosystems. We contrast a for-profit ( ANDROID) system having a hybrid contribution style, with a not-for-profit ( LINUX kernel) system having an open contribution style. To guide our comparisons, we base our analysis on a conceptual model of contribution management that we derived from a total of seven major open-source software systems. A quantitative comparison based on data mined from the ANDROID code review system and the LINUX kernel code review mailing lists shows that both projects have significantly different contribution management styles, suited to their respective market goals, but with individual advantages and disadvantages that are important for practitioners. Contribution management is a real-world problem that has received very little attention from the research community so far. Both studied systems ( LINUX and ANDROID) employ different strategies and techniques for managing contributions, and both approaches are valuable examples for practitioners. Each approach has specific advantages and disadvantages that need to be carefully evaluated by practitioners when adopting a contribution management process in practice. [ABSTRACT FROM AUTHOR]

Editor-in-chief Forrest Shull talks about the practical application of software analytics. [ABSTRACT FROM PUBLISHER]