Marta Cimitile

How changes affect software entropy: an empirical study

Context Software systems continuously change for various reasons, such as adding new features, fixing bugs, or refactoring. Changes may either increase the source code complexity and disorganization, or help to reducing it. Aim This paper empirically investigates the relationship of source code complexity and disorganization—measured using source code change entropy—with four factors, namely the presence of refactoring activities, the number of developers working on a source code file, the participation of classes in design patterns, and the different kinds of changes occurring on the system, classified in terms of their topics extracted from commit notes. Method We carried out an exploratory study on an interval of the life-time span of four open source systems, namely ArgoUML, Eclipse-JDT, Mozilla, and Samba, with the aim of analyzing the relationship between the source code change entropy and four factors: refactoring activities, number of contributors for a file, participation of classes in design patterns, and change topics. Results The study shows that (i) the change entropy decreases after refactoring, (ii) files changed by a higher number of developers tend to exhibit a higher change entropy than others, (iii) classes participating in certain design patterns exhibit a higher change entropy than others, and (iv) changes related to different topics exhibit different change entropy, for example bug fixings exhibit a limited change entropy while changes introducing new features exhibit a high change entropy. Conclusions Results provided in this paper indicate that the nature of changes (in particular changes related to refactorings), the software design, and the number of active developers are factors related to change entropy. Our findings contribute to understand the software aging phenomenon and are preliminary to identifying better ways to contrast it.

Online Discovery of Declarative Process Models from Event Streams

Todays business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state but, due to changing circumstances, they evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period of time or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using synthetic logs and a real-life event log pertaining to the treatment of patients diagnosed with cancer in a large Dutch academic hospital.

Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models

Today’s business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using logs pertaining the health insurance claims handling in a travel agency.

Generating Event Logs Through the Simulation of Declare Models

In the process mining field, several techniques have been developed during the last years, for the discovery of declarative process models from event logs. This type of models describes processes on the basis of temporal constraints. Every behavior that does not violate such constraints is allowed, and such characteristic has proven to be suitable for representing highly flexible processes. One way to test a process discovery technique is to generate an event log by simulating a process model, and then verify that the process discovered from such a log matches the original one. For this reason, a tool for generating event logs starting from declarative process models becomes vital for the evaluation of declarative process discovery techniques. In this paper, we present an approach for the automated generation of event logs, starting from process models that are based on Declare, one of the most used declarative modeling languages in the process mining literature. Our framework bases upon the translation of Declare constraints into regular expressions and on the utilization of Finite State Automata for the simulation. An evaluation of the implemented tool is presented, showing its effectiveness in both the generation of new logs and the replication of the behavior of existing ones. The presented evaluation also shows the capability of the tool of generating very large logs in a reasonably small amount of time, and its integration with state-of-the-art Declare modeling and discovery tools.

Using Declarative Workflow Languages to Develop Process-Centric Web Applications

Nowadays, process-centric Web Applications (WAs) are extensively used in contexts where multi-user, coordinated work is required. Recently, Model Driven Engineering (MDE) techniques have been investigated for the development of this kind of applications. However, there are still some open issues. First, a complete roundtrip engineering support is still missing. Second, the lack of a proper integration of the existing MDE methodologies with workflow modeling techniques does not allow the developers to model the components of a WA in a compact and easy way. Third, the existing MDE approaches are based on procedural languages and not on declarative languages, even if these are more suitable to describe processes characterized by high variability. To address these issues, in this paper, we adopt a conservative approach to roundtrip engineering for the development of process-centric WAs and propose the integration of three MDE metamodels used to represent the main components of a WA with the metamodel of Declare, a declarative language to represent business processes. The proposed approach has been used to develop an online system for visit reservation in a hospital in order to validate its feasibility, correctness and effectiveness.

A model-driven graph-matching approach for design pattern detection

In this paper an approach to automatically detect Design Patterns (DPs) in Object Oriented systems is presented. It allows to link systems source code components to the roles they play in each pattern. DPs are modelled by high level structural properties (e.g. inheritance, dependency, invocation, delegation, type nesting and membership relationships) that are checked against the system structure and components. The proposed metamodel also allows to define DP variants, overriding the structural properties of existing DP models, to improve detection quality. The approach was validated on an open benchmark containing several open-source systems of increasing sizes. Moreover, for other two systems, the results have been compared with the ones from a similar approach existing in literature. The results obtained on the analyzed systems, the identified variants and the efficiency and effectiveness of the approach are thoroughly presented and discussed.

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.

Web applications design recovery and evolution with RE‐UWA

This paper presents a semi‐automatic approach for the recovery and evolution of the design of existing Web applications. The proposed approach is structured in two main phases and is based on the Ubiquitous Web Applications (UWA) design framework, a methodology and a set of models and tools for the user‐centered design of multichannel context‐aware Web applications. In the first phase a representative set of the applications front‐end Web pages are analyzed to abstract the ‘as‐is’ design model of the application according to the UWA methodology. In the second phase, the recovered design model is evolved to define the ‘to be’ version of it. This evolution activity considers the up‐to‐date requirements available for the application and UWA design guidelines to identify shortcomings and opportunities of improvement in the ‘as‐is’ design. The reverse modeling phase exploits clustering and clone detection techniques and is supported by the RE‐UWA tool, an Eclipse IDE customized to implement the reverse engineering process defined to extract formal UWA models expressed as instances of a MOF metamodel. The forward design phase is supported by a set of UWA modeling tools that are built on top of the Eclipse Modeling Framework (EMF) and the Eclipse Graphical Modeling Framework (GMF). The proposed design recovery and evolution approach is applied to four real‐world Web applications and the obtained results are also presented in the paper. Copyright

M3D: a tool for the model driven development of web applications

Nowadays, Web Applications (WAs) are complex software systems, used by multiple users with different roles and often developed to support and manage business processes. Due to the changing nature of the supported processes, WAs need to be easily and quickly modified, to adapt and align them to the processes they support. In recent years, Model Driven Engineering (MDE) approaches have been proposed and used to develop and evolve WAs. However, the definition of appropriate MDE approaches for the development of flexible process-centric WAs is still limited. In particular, (flexible) workflow models have never been integrated with the models (e.g., presentation, information models) used in MDE approaches to develop this type of applications. In this paper, we present M3D (Model Driven Development with Declare), a tool for developing WAs that integrates three MDE metamodels used to represent the main components of a WA with the metamodel of Declare, a declarative language to model business processes. The tool exploits and combines the declarative nature of Declare and the advantages of MDE to get an efficient roundtrip engineering support to develop and evolve flexible process-centric WAs.

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.