EXPLOITING A GENERIC APPROACH TO CONSTRUCT COMPONENT-BASED SYSTEMS SOFTWARE IN LINUX ENVIRONMENTS

Component-based software engineering has recently emerged as a promising solution to the development of system-level software. Unfortunately, current approaches are limited to specific platforms and domains. This lack of generality is particularly problematic as it prevents knowledge sharing and generally drives development costs up. In the past, we have developed a generic approach to component-based software engineering for system-level software called OpenCom. In this paper, we present OpenComL an instantiation of OpenCom to Linux environments and show how it can be profiled to meet a range of system-level software in Linux environments. For this, we demonstrate its application to constructing a programmable router platform and a middleware for parallel environments.

Performance based earthquake engineering: Il metodo P.E.E.R. applicazione su un caso di studio.

L’approccio performance-based nell’Ingegneria sismica è una metodologia di progetto che tiene esplicitamente in conto la performance dell’edificio tra i criteri progettuali. Nell’ambito dei metodi PBEE (Performance-Based Earthquake Engineering) di seconda generazione, quello proposto dal PEER (Pacific Earthquake Engineering Research Center) risulta essere il più diffuso. In esso la performance dell’edificio oggetto di studio viene valutata in termini quantitativi secondo le 3D’s (dollars, deaths, downtime – soldi, decessi, inutilizzo), quantità di notevole interesse per l’utente finale. Il metodo si compone di quattro step, indipendenti tra loro fino alla sintesi finale. Essi sono: l’analisi di pericolosità, l’analisi strutturale, l’analisi di danno, l’analisi delle perdite o di loss. Il risultato finale è la curva di loss, che assegna ad ogni possibile perdita economica conseguente all’evento sismico una probabilità di superamento nell’arco temporale di riferimento. Dopo la presentazione del metodo PEER, si è provveduto ad una sua applicazione su di un caso di studio, nella fattispecie un telaio piano di quattro campate, multipiano, in calcestruzzo armato, costruito secondo le norme del ’92. Per l’analisi di pericolosità si è fatto ricorso alle mappe di pericolosità disponibili sul sito INGV, mentre per l’analisi strutturale si è utilizzato il software open-source OpenSees. Le funzioni di fragilità e quelle di loss sono state sviluppate facendo riferimento alla letteratura scientifica, in particolare il bollettino Fib numero 68 “Probabilistic performance-based seismic design”. In questa sede ci si è concentrati unicamente sulla stima delle perdite economiche, tralasciando le altre due variabili decisionali. Al termine del procedimento si è svolta un’analisi di sensitività per indagare quali parametri influenzino maggiormente la curva di loss. Data la curva di pericolosità, il legame EDP(IM) e la deformazione ultima a collasso risultano essere i più rilevanti sul risultato dell’analisi.

Software Engineering Education Needs More Engineering

To what extent is “software engineering” really “engineering” as this term is commonly understood? A hallmark of the products of the traditional engineering disciplines is trustworthiness based on dependability. But in his keynote presentation at ICSE 2006 Barry Boehm pointed out that individuals’, systems’, and peoples’ dependency on software is becoming increasingly critical, yet that dependability is generally not the top priority for software intensive system producers. Continuing in an uncharacteristic pessimistic vein, Professor Boehm said that this situation will likely continue until a major software-induced system catastrophe similar in impact to the 9/11 World Trade Center catastrophe stimulates action toward establishing accountability for software dependability. He predicts that it is highly likely that such a software-induced catastrophe will occur between now and 2025. It is widely understood that software, i.e., computer programs, are intrinsically different from traditionally engineered products, but in one aspect they are identical: the extent to which the well-being of individuals, organizations, and society in general increasingly depend on software. As wardens of the future through our mentoring of the next generation of software developers, we believe that it is our responsibility to at least address Professor Boehm’s predicted catastrophe. Traditional engineering has, and continually addresses its social responsibility through the evolution of the education, practice, and professional certification/licensing of professional engineers. To be included in the fraternity of professional engineers, software engineering must do the same. To get a rough idea of where software engineering currently stands on some of these issues we conducted two surveys. Our main survey was sent to software engineering academics in the U.S., Canada, and Australia. Among other items it sought detail information on their software engineering programs. Our auxiliary survey was sent to U.S. engineering institutions to get some idea about how software engineering programs compared with those in established engineering disciplines of Civil, Electrical, and Mechanical Engineering. Summaries of our findings can be found in the last two sections of our paper.

The Risk of Using the Q Heterogeneity Estimator for Software Engineering Experiments

All meta-analyses should include a heterogeneity analysis. Even so, it is not easy to decide whether a set of studies are homogeneous or heterogeneous because of the low statistical power of the statistics used (usually the Q test). Objective: Determine a set of rules enabling SE researchers to find out, based on the characteristics of the experiments to be aggregated, whether or not it is feasible to accurately detect heterogeneity. Method: Evaluate the statistical power of heterogeneity detection methods using a Monte Carlo simulation process. Results: The Q test is not powerful when the meta-analysis contains up to a total of about 200 experimental subjects and the effect size difference is less than 1. Conclusions: The Q test cannot be used as a decision-making criterion for meta-analysis in small sample settings like SE. Random effects models should be used instead of fixed effects models. Caution should be exercised when applying Q test-mediated decomposition into subgroups.

Using configuration management and product line software paradigms to support the experimentation process in software engineering.

There is no empirical evidence whatsoever to support most of the beliefs on which software construction is based. We do not yet know the adequacy, limits, qualities, costs and risks of the technologies used to develop software. Experimentation helps to check and convert beliefs and opinions into facts. This research is concerned with the replication area. Replication is a key component for gathering empirical evidence on software development that can be used in industry to build better software more efficiently. Replication has not been an easy thing to do in software engineering (SE) because the experimental paradigm applied to software development is still immature. Nowadays, a replication is executed mostly using a traditional replication package. But traditional replication packages do not appear, for some reason, to have been as effective as expected for transferring information among researchers in SE experimentation. The trouble spot appears to be the replication setup, caused by version management problems with materials, instruments, documents, etc. This has proved to be an obstacle to obtaining enough details about the experiment to be able to reproduce it as exactly as possible. We address the problem of information exchange among experimenters by developing a schema to characterize replications. We will adapt configuration management and product line ideas to support the experimentation process. This will enable researchers to make systematic decisions based on explicit knowledge rather than assumptions about replications. This research will output a replication support web environment. This environment will not only archive but also manage experimental materials flexibly enough to allow both similar and differentiated replications with massive experimental data storage. The platform should be accessible to several research groups working together on the same families of experiments.

Understanding replication of experiments in software engineering: a classification

Context: Replication plays an important role in experimental disciplines. There are still many uncertain- ties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experiment- ers, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? Objective: To improve our understanding of SE experiment replication, in this work we propose a classi- fication which is intend to provide experimenters with guidance about what types of replication they can perform. Method: The research approach followed is structured according to the following activities: (1) a litera- ture review of experiment replication in SE and in other disciplines, (2) identification of typical elements that compose an experimental configuration, (3) identification of different replications purposes and (4) development of a classification of experiment replications for SE. Results: We propose a classification of replications which provides experimenters in SE with guidance about what changes can they make in a replication and, based on these, what verification purposes such a replication can serve. The proposed classification helped to accommodate opposing views within a broader framework, it is capable of accounting for less similar replications to more similar ones regarding the baseline experiment. Conclusion: The aim of replication is to verify results, but different types of replication serve special ver- ification purposes and afford different degrees of change. Each replication type helps to discover partic- ular experimental conditions that might influence the results. The proposed classification can be used to identify changes in a replication and, based on these, understand the level of verification.