Search

Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data-driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared. Junta de Andalucía, Grant/Award Number: P20‐00091; AEI, Grant/Award Number: PID2019‐106758GB‐32/AEI/10.13039/501100011033; Spanish project, Grant/Award Number: PDC2021‐121195‐I00; Spanish Program, Grant/Award Number: BEAGAL18/00064

Due to recent industry needs, synergies between data science and software engineering are starting to be present in data science and engineering academic programs. Two synergies are: applying data science to manage the quality of the software (software analytics) and applying software engineering best practices in data science projects to ensure quality attributes such as maintainability and reproducibility. The lack of these synergies on academic programs have been argued to be an educational problem. Hence, it becomes necessary to explore how to teach software analytics and software engineering best practices in data science programs. In this context, we provide hands-on for conducting laboratories applying project-based learning in order to teach software analytics and software engineering best practices to data science students. We aim at improving the software engineering skills of data science students in order to produce software of higher quality by software analytics. We focus in two skills: following a process and software engineering best practices. We apply project-based learning as main teaching methodology to reach the intended outcomes. This teaching experience shows the introduction of project-based learning in a laboratory, where students applied data science and best software engineering practices to analyze and detect improvements in software quality. We carried out a case study in two academic semesters with 63 data science bachelor students. The students found the synergies of the project positive for their learning. In the project, they highlighted both utility of using a CRISP-DM data mining process and best software engineering practices like a software project structure convention applied to a data science project. This paper was partly funded by a teaching innovation project of ICE@UPC-BarcelonaTech (entitled ‘‘Audiovisual and digital material for data engineering, a teaching innovation project with open science’’), and the ‘‘Beatriz Galindo’’ Spanish Program BEA-GAL18/00064.

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.

Static code analysis tools are designed to aid software developers to build better quality software in less time, by detecting defects early in the software development life cycle. Even the most experienced developer regularly introduces coding defects. Identifying, mitigating and resolving defects is an essential part of the software development process, but frequently defects can go undetected. One defect can lead to a minor malfunction or cause serious security and safety issues. This is magnified in the development of the complex parallel software required to exploit modern heterogeneous multicore hardware. Thus, there is an urgent need for new static code analysis tools to help in building better concurrent and parallel software. The paper reports preliminary results about the use of Appentra’s Parallelware technology to address this problem from the following three perspectives: finding concurrency issues in the code, discovering new opportunities for parallelization in the code, and generating parallel-equivalent codes that enable tasks to run faster. The paper also presents experimental results using well-known scientific codes and POWER systems. This work has been partly funded from the Spanish Ministry of Science and Technology (TIN2015-65316-P), the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya (MPEXPAR: Models de Programació i Entorns d’Execució Parallels, 2014-SGR-1051), and the European Union’s Horizon 2020 research and innovation program throughgrant agreements MAESTRO (801101) and EPEEC (801051).

Background: Quality requirements (QRs) describe desired system qualities, playing an important role in the success of software projects. In the context of agile software development (ASD), where the main objective is the fast delivery of functionalities, QRs are often ill-defined and not well addressed during the development process. Software analytics tools help to control quality though the measurement of quality-related software aspects to support decision-makers in the process of QR management. Aim: The goal of this research is to explore the benefits of integrating a concrete software analytics tool, Q-Rapids Tool, to assess software quality and support QR management processes. Method: In the context of a technology transfer project, the Softeam company has integrated Q-Rapids Tool in their development process. We conducted a series of workshops involving Softeam members working in the Modelio product development. Results: We present the Quality Feedback Loop (QFL) process to be integrated in software development processes to control the complete QR life-cycle, from elicitation to validation. As a result of the implementation of QFL in Softeam, Modelio's team members highlight the benefits of integrating a data analytics tool with their project planning tool and the fact that project managers can control the whole process making the final decisions. Conclusions: Practitioners can benefit from the integration of software analytics tools as part of their software development toolchain to control software quality. The implementation of QFL promotes quality in the organization and the integration of software analytics and project planning tools also improves the communication between teams., Comment: 9 pages, Accepted for publication in IEEE/ACM 43nd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), IEEE, 2021

[Context and motivation] Requirements reuse has been proposed asa key asset for requirements engineers to efficiently elicit, validate and document softwarer equirements and, as a consequence, obtain requirements specifications of better quality through more effective engineering processes.[Question/problem] Regardless the impact requirements reuse could have in software projects’ suc-cess and efficiency, the requirements engineering community has published veryfew studies reporting the way in which this activity is conducted in industry. [Principal ideas/results] In this paper, we present the results of an interview-based study involving 24 IT professionals on whether they reuse requirementsor not and how. Some kind of requirements reuse is carried out by the majorityof respondents, being organizational and project-related factors the main drivers.Quality requirements are the type most reused. The most common strategy isfind-copy-paste-adapt. Respondents agreed that requirements reuse is beneficial,especially for project-related reasons. The most stated challenge to overcome inrequirements reuse is related to the domain of the project and the development of acompletely new system. [Contribution] With this study, we contribute to the stateof the practice in the reuse of requirements by showing how real organizationscarry out this process and the factors that influence it. This work has been partially funded by the Horizon 2020 project OpenReq, which is supported by the European Union under the Grant Nr. 732463.

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: Practitioners would like to take action based on software metrics, as long as they find them reliable. Existing literature explores how metrics can be made reliable, but remains unclear if there are other conditions necessary for a metric to be actionable. Context & Method: In the context of a European H2020 Project, we conducted a multiple case study to study metrics’ use in four companies, and identified instances where these metrics influenced actions. We used an online questionnaire to enquire about the project participants’ views on actionable metrics. Next ,we invited one participant from each company to elaborate on the identified metrics’ use for taking actions and the questionnaire responses (N=17). Result:We learned that a metric that is practical, contextual, and exhibits high data quality characteristics is actionable. Even a non-actionable metric can be useful, but an actionable metric mostly requires interpretation. However, the more these metrics are simple and reflect the software development context accurately, the less interpretation required to infer actionable information from the metric. Company size and project characteristics can also influence the type of metric that can be actionable. Conclusion: This exploration of industry’s views on actionable metrics help characterize actionable metrics in practical terms. This awareness of what characteristics constitute an actionablemetric can facilitate theirdefinition and developmentright from the start of a software metrics program This work is a result of the Q-Rapids Project, funded by the European Union’s Horizon 2020 research and innovation program, under grant agreement No. 732253.

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

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 the software quality has also been the key targets 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 paper 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 a year, 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. The 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 the 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.

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.

Understanding the impact of soft errors on applications can be expensive. Often, it requires an extensive error injection campaign involving numerous runs of the full application in the presence of errors. In this paper, we present a novel approach to arriving at the ground truth-the true impact of an error on the final output-for iterative methods by observing a small number of iterations to learn deviations between normal and error-impacted execution. We develop a machine learning based predictor for three iterative methods to generate ground-truth results without running them to completion for every error injected. We demonstrate that this approach achieves greater accuracy than alternative prediction strategies, including three existing soft error detection strategies. We demonstrate the effectiveness of the ground truth prediction model in evaluating vulnerability and the effectiveness of soft error detection strategies in the context of iterative methods. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number 66905, program manager Lucy Nowell. Pacific Northwest National Laboratory is operated by Battelle for DOE under Contract DE-AC05-76RL01830.

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

Analyses of the interactions among quality requirements (QRs) have often found that optimizing on one QR will cause serious problems with other QRs. As just one relevant example, one large project had an Integrated Product Team optimize the system for Security. In doing so, it reduced its vulnerability profile by having a single-agent key distribution system and a single copy of the data base – only to have the Reliability engineers point on that these were system-critical single points of failure. The project’s Security-optimized architecture also created conflicts with the system’s Performance, Usability, and Modifiability. Of course, optimizing the system for Security had synergies with Reliability in having high levels of Confidentiality, Integrity, and Availability. This panel aims at fostering discussion on these relationships among QRs and how the use of data repositories may help discovering them.

Executable models play a key role in many software development methods by facilitating the (semi)automatic implementation/execution of the software system under development. This is possible because executable models promote a complete and fine-grained specification of the system behaviour. In this context, where models are the basis of the whole development process, the quality of the models has a high impact on the final quality of software systems derived from them. Therefore, the existence of methods to verify the correctness of executable models is crucial. Otherwise, the quality of the executable models (and in turn the quality of the final system generated from them) will be compromised. In this paper a lightweight and static verification method to assess the correctness of executable models is proposed. This method allows us to check whether the operations defined as part of the behavioural model are able to be executed without breaking the integrity of the structural model and returns a meaningful feedback that helps repairing the detected inconsistencies. HighlightsChecking the quality of a model guarantees the quality of the final software system.Lightweight and static verifier to check the correctness of executable models.We focus on executability property of operations.Our method returns valuable feedback.

Resulta difícil entendre, i més encara millorar, la qualitat del programari sense conèixer el procés de desenvolupament del mateix i dels seus productes. És necessari dur a terme algun tipus de procés d’avaluació per tal de determinar la qualitat dels productes (software). La investigació que es descriu en el present estudi és un intent d’analitzar diversos paràmetres en la producció del desenvolupament del programari i mostrar-los en una ”dashboard”. Es realitza, per tant, un breu recorregut per l’enginyeria, la qualitat i els paràmetres del programari en l’entorn d’una empresa informàtica real (Reval). Els resultats principals d’aquests paràmetres apareixeran monitoritzats en una única pàgina, de fàcil lectura, en una interfície a temps real per a l’usuari, la qual mostrarà una gràfica de l’estat actual (snapshot) i un historial dels indicadors clau de la producció software que permetran realitzar decisions instantànies i informades d’una ullada. L’analsis mètric, tot i no ser un tema interessant, és un aspecte necessari. És part de la sobrecàrrega necessària per a realitzar l’entrega del producte. Si podem detectar els possibles riscs de l’entrega a temps, els podrem solucionar. Si no descobrim quina és la seva magnitud, estem anant a les palpentes i els projectes poden fracassar. És important mesurar els aspectes adequats i assegurar-nos que van pel bon camí (per dirigir un treball progressivament). En segon lloc, és important mesurar els esforços per poder millorar ja que per molt que canviem coses i ens sembli que ho fem bé, no coneixerem les millores reals i no les podrem qualificar.

We advance a general technique for enriching logical systems with soft constraints, making them suitable for specifying complex software systems where parts are put together not just based on how they meet certain functional requirements but also on how they optimise certain constraints. This added expressive power is required, for example, for capturing quality attributes that need to be optimised or, more generally, for formalising what are usually called service-level agreements. More specifically, we show how institutions endowed with a graded semantic consequence can accommodate soft-constraint satisfaction problems. We illustrate our approach by showing how, in the context of service discovery, one can quantify the compatibility of two specifications and thus formalise the selection of the most promising provider of a required resource.

Software quality models provide a framework to measure and evaluate software quality of software systems. They are the basis upon which classify requirements and may be eventually used to guide the quantification of these requirements, especially non-functional requirements. Lots of approaches for building quality models have been proposed in the last decades, but still their reuse along different projects is a challenge. In this paper we present several types of knowledge repositories and reuse processes to bridge this gap. The approach implements the idea of software factory and uses some well-known standards and notations like ISO/IEC 25010 as quality standard and the i* framework to codify knowledge patterns. We will illustrate how this reusebased approach helps in obtaining composite quality models for systems that integrate several software components with an individual quality model each.

MATE (Monitoring, Analysis and Tuning Environment) es un proyecto que surge en 2004 como tesis doctoral de Anna Sikora con el propósito de investigar la mejora de rendimiento de aplicaciones paralelas a través de la modificación dinámica. Nuestro proyecto supone un paso adelante en cuestiones de calidad de software y pretende dotar al proyecto MATE de una base de desarrollo sólida de cara a futuras lineas de trabajo. Para ello se hace frente a la problemática desde tres perspectivas: la creación de una metodología de desarrollo (y su aplicación sobre el proyecto existente), la implantación de un entorno de desarrollo de soporte y el desarrollo de nuevas características para favorecer la portabilidad y la usabilidad, entre otros aspectos. MATE (Monitoring, Analysis and Tuning Environment) és un projecte que sorgeix al 2004 com a tesi doctoral de l'Anna Sikora amb el propòsit d'investigar la millora del rendiment d'aplicacions paral·leles a través de la modificació dinàmica. El nostre projecte suposa un pas endavant en termes de qualitat de software i pretén dotar al projecte MATE d'una base de desenvolupament sòlida de cara a futures línies de treball. Per això es fa front a la problemàtica des de tres perspectives: la creació d'una metodologia de desenvolupament (i la seva aplicació al projecte existent), la implantació d'un entorn de desenvolupament de suport i el desenvolupament de noves característiques per afavorir la portabilitat i la usabilitat entre altres aspectes. MATE (Monitoring, Analysis and Tuning Environment) is a project that came up in 2004 as the Anna Sikora Ph.D. thesis with the purpose of investigating in the improvement of parallel applications performance through dynamic tuning. Our project means a step forward in software quality concepts and expects to provide the MATE project with a solid development base for the forthcoming lines of work. With this purpose, we face the problem in three ways: the creation of a development methodology (and its implementation in the current project), the deployment of a support development environment and the implementation of new features to improve the portability and the usability among other aspects.

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.

In this paper, we present a detailed evaluation of a set of well-known Machine Learning classifiers in front of dynamic and non-deterministic software anomalies. The system state prediction is based on monitoring system metrics. This allows software proactive rejuvenation to be triggered automatically. Random Forest approach achieves validation errors less than 1% in comparison to the well-known ML algorithms under avaluation. In order to reduce automatically the number of monitored parameters, needed to predict software anomalies, we analyze Lasso Regularization technique jointly with the Machine Learning classifiers to evaluate how the prediction accuracy could be guaranteed within an acceptable threshold. This allows to reduce drastically (around 60% in the best case) the number of monitoring parameters. The framework, based on ML and Lasso regularization techniques, has been validated using an e-commerce environment with Apache Tomcat server, and MySql database server.

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.

En los últimos años se constata una tendencia creciente por parte de las organizaciones a desarrollar sus sistemas software mediante la combinación de componentes, en lugar de desarrollar dichos sistemas partiendo de cero. Esta tendencia es debida a varios factores. Entre ellos cabe destacar: la necesidad de las organizaciones de reducir los costes y el tiempo dedicados al desarrollo de los sistemas software; el crecimiento del mercado de componentes software; la reducción de la distancia entre clientes y proveedores de software gracias a la creación de nuevos canales de comunicación y marketing (p.e., www.componentsource.com); y la existencia de tecnologías que facilitan el desarrollo de sistemas basados en componentes.

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.