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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.

Los requisitos de calidad (también llamados requisitos no-funcionales) son aquellos que permiten asegurar la calidad del software. Incluyen aspectos muy diversos, como disponibilidad, seguridad, rendimiento, escalabilidad, portabilidad y usabilidad, entre otros. Los continuos avances tecnológicos, como el software en la nube o el Internet de las Cosas, presentan nuevos retos en el desarrollo del software para poder garantizar un nivel de calidad satisfactorio de dichos aspectos. Asimismo, las metodologías de desarrollo ágil, cuyo uso viene en aumento, tales como SCRUM, XP, Kanban, no dan el soporte necesario para la gestión de dichos requisitos de calidad. Con el fin de facilitar a los ingenieros del software la toma de decisiones sobre el nivel de calidad necesario en un proyecto, es imprescindible conocer de antemano: 1) qué criterios se van a tener en cuenta para verificar, priorizar, planificar y/o negociar los requisitos de calidad. Asimismo, es necesario: 2) precisar cómo se van a evaluar dichos criterios, y 3) identificar qué factores del contexto del proyecto pueden afectar dicha evaluación. Para intentar dar respuesta a estas 3 cuestiones o preguntas de investigación, los autores de este capítulo han diseñado y están llevando a cabo un estudio sistemático de la literatura. Este trabajo presenta para su discusión la descripción de la metodología seguida en ese estudio, así como algunos de los resultados preliminares obtenidos durante su ejecución.

Background: Despite the growth in the use of software analytics platforms in industry, little empirical evidence is available about the challenges that practitioners face and the value that these platforms provide. Aim: The goal of this research is to explore the benefits of using a software analytics platform for practitioners managing quality. Method: In a technology transfer project, a software analytics platform was incrementally developed between academic and industrial partners to address their software quality problems. This paper focuses on exploring the value provided by this software analytics platform in two pilot projects. Results: Practitioners emphasized major benefits including the improvement of product quality and process performance and an increased awareness of product readiness. They especially perceived the semi-automated functionality of generating quality requirements by the software analytics platform as the benefit with the highest impact and most novel value for them. Conclusions: Practitioners can benefit from modern software analytics platforms, especially if they have time to adopt such a platform carefully and integrate it into their quality assurance activities., Comment: This is an Author's Accepted Manuscript of a paper consisting of a post-peer-review, pre-copyedit version of a paper accepted at the 13th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2019. The final authenticated version is available online: https://ieeexplore.ieee.org/document/8870162

This work reports the objectives, current state, and outcomes of the Q-Rapids H2020 project. Q-Rapids (Quality-Aware Rapid Software Development) proposes a data-driven approach to the production of software following very short development cycles. The focus of Q-Rapids is on quality aspects, represented through quality requirements. The Q-Rapids platform, which is the tangible software asset emerging from the project, mines software repositories and usage logs to identify candidate quality requirements that may ameliorate the values of strategic indicators like product quality, time to market or team productivity. Four companies are providing use cases to evaluate the platform and associated processes.

Software quality poses continuously new challenges in software development, including aspects related to both software development and system usage, which significantly impact the success of software systems. The Q-Rapids H2020 project defines an evidence-based, data-driven quality-aware rapid software development methodology. Quality requirements (QRs) are incrementally elicited, refined and improved based on data gathered from software repositories, project management tools, system usage and quality of service. This data is analysed and aggregated into quality-related key strategic indicators (e.g., development effort required to include a given QR in the next development cycle) which are presented to decision makers using a highly informative dashboard. The Q-Rapids platform is being evaluated in-premises by the four companies participating in the consortium, reporting useful lessons learned and directions for new development.

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage., This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available online

The increasing device count and design complexity are posing significant challenges to post-silicon validation. Bug diagnosis is the most difficult step during post-silicon validation. Limited reproducibility and low testing speeds are common limitations in current testing techniques. Moreover, low observability defies full-speed testing approaches. Modern solutions like on-chip trace buffers alleviate these issues, but are unable to store long activity traces. As a consequence, the cost of post-Si validation now represents a large fraction of the total design cost. This work describes a hybrid post-Si approach to validate a modern load-store queue. We use an effective error detection mechanism and an expandable logging mechanism to observe the microarchitectural activity for long periods of time, at processor full-speed. Validation is performed by analyzing the log activity by means of a diagnosis algorithm. Correct memory ordering is checked to root the cause of errors.

As long as virtualization has been introduced in data centers, it has been opening new chances for resource management. Now, it is not just used as a tool for consolidating underused nodes and save power, it also allows new solutions to well-known challenges, such as fault tolerance or heterogeneity management. Virtualization helps to encapsulate Web-based applications or HPC jobs in virtual machines and see them as a single entity which can be managed in an easier way. This paper proposes a new scheduling policy to model and manage a virtualized data center which mainly focuses on the allocation of VMs in data center nodes according to multiple facets while optimizing the provider’s profit. In particular, it considers energy efficiency, virtualization overheads, fault tolerance, and SLA violation penalties, while adding the ability to outsource resources to external providers. Using our approach, a data center can improve the provider’s benefit by 15% and get a power reduction while solving well-known challenges, such as fault tolerance and outsourcing, in a better a more intuitive way that typical approaches do.

Technology scaling leads to burn-in phase out and increasing post-silicon test complexity, which increases in-the-field error rate due to both latent defects and actual errors. As a consequence, there is an increasing need for continuous on-line testing techniques to cope with hard errors in the field. Similarly, those techniques are needed for detecting soft errors in logic, whose error rate is expected to raise in future technologies. Cache memories, which occupy most of the area of the chip, are typically protected with parity or ECC, but most of the wires as well as some combinational blocks remain unprotected against both soft and hard errors. This paper presents a set of techniques to detect and confine hard and soft errors in cache memories in combination with parity/ECC at very low cost. By means of hard signatures in data rows and error tracking, faults can be detected, classified properly and confined for hardware reconfiguration.

Selection of software packages from user requirements is a central task in software engineering. Selection of inappropriate packages may compromise business processes and may interfere negatively in the functioning of the involved organization. Success of package selection is endangered because of many factors, one of the most important being the absence of structured descriptions of both package features and user quality requirements. In this paper, we propose a methodology for describing quality factors of software packages using the ISO/IEC quality standard as a framework. Following this standard, relevant attributes for a specific software domain are identified and structured as a hierarchy, and metrics for them are chosen. Software packages in this domain can then be described in a uniform and comprehensive way. Therefore, selection of packages can be ameliorated by transforming user quality requirements into requirements expressed in terms of quality model attributes. We illustrate the approach by presenting, in some depth, a quality model for the mail server domain.

The existence of a standard container library has been largely recognized as a key feature for improving the quality and effectiveness of Ada programming. In this paper, we aim at providing a quality model for making explicit the quality features (those concerning functionality, suitability, etc.) that determine the form that such a library might take. Quality features are arranged hierarchically according to the ISO/IEC quality standard. We tailor this standard to the specific context of container libraries, by identifying their observable attributes and establishing some tradeoffs among them. Afterwards, we apply the resulting model to a pair of existing container libraries. As main contribution of our proposal, we may say that the resulting quality model provides a structured framework for (1) discussing and evaluating the capabilities that the prospective Ada Standard Container Library might offer, and (2) analyzing the consequences of the decisions taken during its design.

Systematic formulation of software process models (SPM) is currently a challenging problem in software engineering. We present an approach to define such models that encourages: reuse of both elements and models; modularity and incrementality in model construction; simplicity and naturality of the resulting model; and a high degree of concurrence in their enaction. We focus on model definition, distinguishing as usual its static and dynamic parts. We define the static part by means of formally defined hierarchies introducing the categories of elements that take part in SPM definition. Such hierarchies may be constructed and enlarged according to the requirements of any specific SPM. We present as an example a hierarchy for component programming that takes into account non-functional aspects of software (efficiency, etc.). The dynamic part of the SPM is defined by means of precedence relationships between tasks that take part in the model. These precedence relationships are represented with precedence graphs. Development strategies are defined by encapsulating new precedence relationships in modules, that can be combined and reused.

The use of quality models in software package procurement provides a framework for the description of the domain which the package belongs to. Package descriptions and user quality requirements may be translated into the quality concepts defined in the model making package procurement more efficient and reliable. In this paper we address the construction of quality models for complex software domains, defined as domains that imply a mixture of functionalities. Procurement processes taking place in complex domains require not a single package to be selected but a set of them. As a consequence, instead of a standard, single quality model, we need a more elaborated quality model for driving the simultaneous procurement of multiple software packages. We describe the parts that compose these kind of models, the methodology for building them and their usage in software procurement. We apply the approach to the complex domain of mail server systems.

Unplanned system outages have a negative impact on company revenues and image. While the last decades have seen a lot of efforts from industry and academia to avoid them, they still happen and their impact is increasing. According to many studies, one of the most important causes of these outages is software aging. Software aging phenomena refers to the accumulation of errors, usually provoking resource contention, during long running application executions, like web applications, which normally cause applications/systems hang or crash. Determining the software aging root cause failure, not the resource or resources involved in, is a huge task due to the growing day by day complexity of the systems. In this paper we present a monitoring framework based on Aspect Programming to monitor the resources used by every application component in runtime. Knowing the resources used by every component of the application we can determine which components are related to the software aging. Furthermore, we present a case study where we evaluate our approach to determine in a web application scenario, which components are involved in the software aging with promising results.

Software component selection is growing in importance. Its success relies on correctly assessing the candidate components' quality. For a particular project, you can assess quality by identifying and analyzing the criteria that affect it. Component selection is on the suitability and completeness of the criteria used for evaluation. Experiences from determining criteria for several industrial projects provide important lessons. For a particular selection process, you can organize selection criteria into a criteria catalog. A CC is built for a scope, which can be either a domain (workflow systems, mail servers, antivirus tools, and so on) or a category of domains (communication infrastructure, collaboration software, and so on). Structurally, a CC arranges selection criteria in a hierarchical tree-like structure. The higher-level selection criteria serve to classify more concrete selection criteria, usually allowing some overlap. They also serve to leverage the CC.

The growing importance of commercial off-the-shelf software packages requires adapting some software engineering practices, such as requirements elicitation and testing, to this emergent framework. Also, some specific new activities arise, among which selection of software packages plays a prominent role. All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages' capabilities. There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback.

El uso de modelos de calidad durante la selección de componentes COTS (Comercial Off-The-Shelf) proporciona un entorno adecuado para la descripción de los dominios a los que éstos pertenecen. En este artículo tratamos la construcción de modelos de calidad para Sistemas Software basados en Componentes COTS (SSCC), que definiremos como sistemas compuestos por varios componentes COTS interconectados. Los procesos de selección llevados a cabo para obtener un SSCC requieren la selección de varios productos COTS. Proponemos un método para la construcción de modelos de calidad para SSCC basado en la aplicación de cuatro actividades. Nuestro objetivo es conseguir que los factores de calidad que aparecen en los SSCC estén definidos en términos de los factores de calidad de los componentes COTS y, de esta forma, obtener eficientemente modelos de calidad y sean fáciles de entender, analizar, mantener y reutilizar.

Most adaptive systems have compensation mechanisms for recovering from or preventing failures. However, sometimes a compensation is not essential. Hence, diagnosing and compensating each and every one of their failures may be ineffective. Rather than polluting a requirements specification with fine grained definition of failure-handling conditions, this work aims to increase the flexibility of failure handling in self-adaptive systems using tolerance policies. We allow the expression of conditions in which certain failures may be ignored – i.e., conditions on which a failure will not be compensated. Such policies may lead to reduced costs and performance improvement. The FAST framework consists of the definition of a tolerance policy, the mechanisms to evaluate this policy and a tool to aid the creation of policies.

The selection of COTS components is made not only by an analysis of their technical quality but also (and sometimes mostly) by considering how they fulfill the nontechnical requirements considered relevant, which refer to licensing, reputation, and similar issues. In this paper we present an approach for managing non-technical requirements during COTS selection. The proposal is based on extending the ISO/IEC 9126-1 catalogue of quality factors by adding factors related to non-technical issues, obtaining a cohesive and comprehensive framework for managing requirements during selection. We show how we can use this catalogue for eliciting non-technical requirements, and how the metrics included in the catalogue may be used to define fit criteria for requirements. We illustrate our approach with an industrial experience that took place in the telecommunications field.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Automatic source-code inspection tools help to assess, monitor and improve code quality. Since these tools only examine the software project’s codebase, they overlook other possible factors that may impact code quality and the assessment of the technical debt (TD). Our initial hypothesis is that human factors associated with the software developers, like coding expertise, communication skills, and experience in the project have some measurable impact on the code quality. In this exploratory study, we test this hypothesis on two large open source repositories, using TD as a code quality metric and the data that may be inferred from the version control systems. The preliminary results of our statistical analysis suggest that the level of participation of the developers and their experience in the project have a positive correlation with the amount of TD that they introduce. On the contrary, communication skills have barely any impact on TD.