Pasquale Ardimento

Multiview Framework for Goal Oriented Measurement Plan Design

The need for systematic evaluation of process quality and of the resulting products has always been an issue of primary importance within the software engineering community. In the past few years many factors have determined changes in the software process scenario that inevitably impact on software quality. To this end, goal oriented measurement models, such as Goal Question Metrics (GQM), have become essential for assessing desired quality and for enacting software process improvement (SPI). Although the importance of measurement is a shared issue, many software organizations encounter difficulties and strive to define and adopt measurement plans successfully. Causes are most likely attributable to critical issues such as measurement plan dimensions, complexity, dependencies among goals. Often the industrial projects are characterized by GQM plans with numerous quality factors and, consequently, numerous goals. This makes both measurement and interpretation procedures quite onerous. Furthermore, managing a measurement plan turns out to be quite costly and requires numerous investments. To this end, this work proposes a GQM-based approach (Multiview Framework) that provides support in designing a structured measurement plan in order to overcome the common problems mentioned previously, and manage large industrial measurement plans. The proposed approach has been validated through a post mortem analysis, consisting in a legacy data study, carried out on industrial project data.

Empirical Investigation of the Efficacy and Efficiency of Tools for Transferring Software Engineering Knowledge

Continuous pressure on behalf of enterprises leads to a constant need for innovation. This involves exchanging results of knowledge and innovation among research groups and enterprises in accordance to the Open Innovation paradigm. The technologies that seem to be apparently attractive for exchanging knowledge are the Internet and its search engines. Literature provides many discordant opinions on their efficacy, and no empirical evidence on the topic. This work starts from the definition of a Knowledge Acquisition Process, and presents a rigorous empirical investigation that evaluates the efficacy of the previous technologies within the Exploratory Search of Knowledge and of Relevant Knowledge according to specific knowledge requirements. The investigation has pointed out that these technologies are not effective for Explorative Search. The paper concludes with a brief analysis of other technologies to develop and analyse in order to overcome the weaknesses that this investigation has pointed out within the Knowledge Acquisition Process.

Knowledge Management Integrated with e-learning in Open Innovation

This paper presents a framework aiming to support an «innovation chain» in an Open Innovation (OI) perspective. In order to transfer research results from producers to users, it is necessary to develop a Knowledge Manage-ment System supporting formalization, packaging and characterization to be able to select, understand and collect research results and/or innovations deriving from them. Suitable skills are required to transfer and collect innovation. Since in OI the knowledge producer and fi nal users are by defi nition geographically distant, the required specialist skills have to be acquired through an e-learning system. This system must offer Learning Objects that can be combined within a course that also takes into account the user’s past experiences. This work proposes an approach based on the integration of these two systems, and presents PROMETHEUS, a tool supporting this approach. The results of preliminary experimentation highlighted the strengths and weaknesses of the approach. They will be used to plan further experimentation and initiatives serving to facilitate the transfer of research results from state of the art to state of practice.

Maintenance-oriented selection of software components

Component-based software engineering is a new, promising, and rapidly growing discipline in both academia and industry. However, maintaining component-based systems (CBSs) introduces new issues: the choice of the components requires identifying a set of parameters that characterize them, in order to select the appropriate ones for a specific software system. In our research we propose a characterization of components aimed at foreseeing the maintenance effort of the CBS. Here we perform an empirical study in the context of three industrial software projects to assess these parameters. Our experience suggests a number of components characteristics, which can be useful for the purpose above. Moreover, the study produced some lessons learned, useful for building software applications easy to maintain. The results show that the lessons learned could be generalized, although further empirical studies are required.

Empirical Validation of Knowledge Packages as Facilitators for Knowledge Transfer

Transfer of research results in production systems requires, among others, that knowledge be explicit and understandable by stakeholders. Such transfer is demanding, as so many researchers have been studying alternative ways to classic approaches such as books and papers that favour knowledge acquisition on behalf of users. In this context, we propose the concept of Knowledge Experience Package (KEP) with a specific structure as an alternative. The KEP contains both the conceptual model(s) of the research results which make up the innovation, including all the necessary documentation ranging from papers or book chapters; and the experience collected in acquiring it in business processes, appropriately structured. The structure allows the identification of the knowledge chunk(s) that the developer, who is acquiring the knowledge, needs in order to simplify the acquisition process. The experience is needed to point out the scenarios that the user will most likely face and therefore refer to. Both structure and experience are important factors for the innovation transferability and efficacy. Furthermore, we have carried out an experiment which compared the efficacy of this instrument with the classic ones, along with the comprehensibility of the information enclosed in a KEP rather than in a set of Papers. The experiment has pointed out that knowledge packages are more effective than traditional ones for knowledge transfer.

Assessing multiview framework (MF) comprehensibility and efficiency: A replicated experiment

Goal oriented quality models have become an important means for assessing and improving software quality. In previous papers, the authors have proposed an approach called multiview framework, for guiding quality managers in designing and managing a goal oriented quality model. This approach has been validated through a controlled experiment carried out with university students. In this paper, the authors discuss a replication of the controlled experiment, carried out with 28 university graduates attending a master degree course in an Italian university. Although research hypotheses are the same, context differs. In the replication, experimental subjects were more representative of practitioners, because their master degree course required project work with industrial partners. Using a cross-over experimental design we found that subjects using the multiview framework made significantly fiewer errors (p<0.05, effect size=1.08) and took significantly less time (p<0.51, effect size=1.82) to review the status of a project than when they used a standard GQM approach. This result was consistent with the results of our original experiment.

An Empirical Study on Software Engineering Knowledge/Experience Packages

This paper is concerned with characterization of software engineering knowledge and experience packages (EP) in the user perspective. It presents the first iteration of an evidence-based study. Results are presented from surveys conducted with many practitioners about the available experience bases, and on literature, to improve our understanding about the state of the practice and art for EP. Additionally, the paper presents attributes and their properties that, in the opinion of the participant practitioners, are relevant for characterizing an EP in the user perspective. Subsequently, with regard to this empirical system, the Acceptability indirect measurement model is provided for experience components. Moreover, the test of this measurement model is shown, which involved both developing qualitative evaluations with practitioners, and measuring ten Internet-available experience bases. Finally, the threats to validity are considered that, as usual for pilot studies, call for further investigation.

A maintenance oriented Framework for software components characterization

Following to the increased size and complexity of software products, a single application is commonly made up of components produced by different developers using different practices. Components can be of different kinds: COTS (commercial off-the-shelf), open source (OS), developed in-home, etc. Use of components places new questions and this paper determines a components maintainability through a framework for software components characterization. It supports a software engineer in selecting components to integrate in a software system. The framework includes three sets of characterization parameters: the first for all types of components; the second and third set for white box and OS components. The framework has been evaluated through an empirical study on two industrial projects. Results are interesting: several of the characteristics included in the framework are correlated and are unable to characterize the system maintainability. These findings seem disappointing although interesting and stimulate further research within component based development

The Lifelong Learning in the University: Learning Networks and Knowledge Transferring

Practitioners must continually update their skills to align their professional profile to market needs and social organizations in which they live, both characterized by extreme variability and volatility. In this scenario, Universities, the traditional Institution for the knowledge transferring, assume the role of an institution dedicated to lifelong learning. However the lifelong learning highlights several issues that make it unsuitable to the university instructional models. In order to face this problem the authors propose to use a Learning Network model integrating a Knowledge Base Experience (Prometheus) to support distribution of contents and to the enhancement knowledge transferring. The results of an empirical experimentation encourage their adoption in real contexts.

Predicting Bug-Fix Time: Using Standard Versus Topic-Based Text Categorization Techniques

In modern software development, finding and fixing bugs is a vital part of software development and quality assurance. Once a bug is reported, it is typically recorded in the Bug Tracking System, and is assigned to a developer to resolve (bug triage). Current practice of bug triage is largely a manual collaborative process, which is often time-consuming and error-prone. Predicting on the basis of past data the time to fix a newly-reported bug has been shown to be an important target to support the whole triage process. Many researchers have, therefore, proposed methods for automated bug-fix time prediction, largely based on statistical prediction models exploiting the attributes of bug reports. However, existing algorithms often fail to validate on multiple large projects widely-used in bug studies, mostly as a consequence of inappropriate attribute selection [2]. In this paper, instead of focusing on attribute subset selection, we explore an alternative promising approach consisting of using all available textual information. The problem of bug-fix time estimation is then mapped to a text categorization problem. We consider a multi-topic Supervised Latent Dirichlet Allocation (SLDA) model, which adds to Latent Dirichlet Allocation a response variable consisting of an unordered binary target variable, denoting time to resolution discretized into FAST (negative class) and SLOW (positive class) labels. We have evaluated SLDA on four large-scale open source projects. We show that the proposed model greatly improves recall, when compared to standard single topic algorithms.