Model Checking to Improve Precision of Design Pattern Instances Identification in OO Systems

In the last two decades some methods and tools have been proposed to identify the Design Pattern (DP) instances implemented in an existing Object Oriented (OO) software system. This allows to know which OO components are involved in each DP instance. Such a knowledge is useful to better understand the system thus reducing the effort to modify and evolve it. The results obtained by the existing methods and tools can suffer a lack of completeness or precision due to the presence of false positive/negative. Model Checking (MC) algorithms can be used to improve the precision of DP's instances detected by a tool by automatically refining the results it produces. In this paper a MC based technique is defined and applied to the results of an existing DPs mining tool, called Design Pattern Finder (DPF), to improve the precision by verifying automatically the DPs instances it detects. To verify and assess the feasibility and the effectiveness of the proposed technique, we carried out a case study where it was applied on some open source OO systems. The results showed that the proposed technique allowed to improve the precision of the DPs instances detected by the DPF tool

Automatic Detection of GUI Design Smells: The Case of Blob Listener

International audienceGraphical User Interfaces (GUIs) intensively rely on event-driven programming: widgets send GUI events, which capture users' interactions, to dedicated objects called controllers. Controllers implement several GUI listeners that handle these events to produce GUI commands. In this work, we conducted an empirical study on 13 large Java Swing open-source software systems. We study to what extent the number of GUI commands that a GUI listener can produce has an impact on the change-and fault-proneness of the GUI listener code. We identify a new type of design smell, called Blob listener that characterizes GUI listeners that can produce more than two GUI commands. We show that 21 % of the analyzed GUI controllers are Blob listeners. We propose a systematic static code analysis procedure that searches for Blob listener that we implement in InspectorGuidget. We conducted experiments on six software systems for which we manually identified 37 instances of Blob listener. InspectorGuidget successfully detected 36 Blob listeners out of 37. The results exhibit a precision of 97.37 % and a recall of 97.59 %. Finally, we propose coding practices to avoid the use of Blob listeners

Functional Size Measurement and Model Verification for Software Model-Driven Developments: A COSMIC-based Approach

Historically, software production methods and tools have a unique goal: to produce high quality software. Since the goal of Model-Driven Development (MDD) methods is no different, MDD methods have emerged to take advantage of the benefits of using conceptual models to produce high quality software. In such MDD contexts, conceptual models are used as input to automatically generate final applications. Thus, we advocate that there is a relation between the quality of the final software product and the quality of the models used to generate it. The quality of conceptual models can be influenced by many factors. In this thesis, we focus on the accuracy of the techniques used to predict the characteristics of the development process and the generated products. In terms of the prediction techniques for software development processes, it is widely accepted that knowing the functional size of applications in order to successfully apply effort models and budget models is essential. In order to evaluate the quality of generated applications, defect detection is considered to be the most suitable technique. The research goal of this thesis is to provide an accurate measurement procedure based on COSMIC for the automatic sizing of object-oriented OO-Method MDD applications. To achieve this research goal, it is necessary to accurately measure the conceptual models used in the generation of object-oriented applications. It is also very important for these models not to have defects so that the applications to be measured are correctly represented. In this thesis, we present the OOmCFP (OO-Method COSMIC Function Points) measurement procedure. This procedure makes a twofold contribution: the accurate measurement of objectoriented applications generated in MDD environments from the conceptual models involved, and the verification of conceptual models to allow the complete generation of correct final applications from the conceptual models involved. The OOmCFP procedure has been systematically designed, applied, and automated. This measurement procedure has been validated to conform to the ISO 14143 standard, the metrology concepts defined in the ISO VIM, and the accuracy of the measurements obtained according to ISO 5725. This procedure has also been validated by performing empirical studies. The results of the empirical studies demonstrate that OOmCFP can obtain accurate measures of the functional size of applications generated in MDD environments from the corresponding conceptual models.Marín Campusano, BM. (2011). Functional Size Measurement and Model Verification for Software Model-Driven Developments: A COSMIC-based Approach [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11237Palanci

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

Code smells detection and visualization: A systematic literature review

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches several methods are used, such as: city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.Comment: submitted to ARC

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Intelligent Data-Driven Reverse Engineering of Software Design Patterns

Recognising implemented instances of Design Patterns (DPs) in software design discloses and recovers a wealth of information about the intention of the original designers and the rationale for their design decisions. Because it is often the case that the documentation available for software systems, if any, is poor and/or obsolete, recovering such information can be of great help and importance for maintenance tasks. However, since DPs are abstractly and vaguely defined, a set of software classes with exactly the same relationships as expected for a DP instance may actually be only accidentally similar. On the other hand, a set of classes with relationships that are, to an extent, different from those typically expected can still be a true DP instance. The deciding factor is mainly concerned with whether or not the set of classes is actually intended to solve the design problem addressed by the DP, thus making the intent a fundamental and defining characteristic of DPs. Discerning the intent of potential instances requires building complex models that cannot be built using only the descriptions of DPs in books and catalogues. Accordingly, a paradigm shift in DP recognition towards fully machine learning based approaches is required. The problem is that no accurate and sufficiently large DP datasets exist, and it is difficult to manually construct one. Moreover, there is a lack of research on the feature set that should be used in DP recognition. The main aim of this thesis is to enable the required paradigm shift by laying down an accurate, comprehensive and information-rich foundation of feature and data sets. In order to achieve this aim, a large set of features is developed to cover a wide range of design aspects, with particular focus on design intent. This set serves as a global feature set from which different subsets can be objectively selected for different DPs. A new and feasible approach to DP dataset construction is designed and used to construct training datasets. The feature and data sets are then used experimentally to build and train DP classifiers. The results demonstrate the accuracy and utility of the sets introduced, and show that fully machine learning based approaches are capable of providing appropriate and well-equipped solutions for the problem of DP recognition.Saudi Cultural Burea