In recent years it has been assessed that in people with type 2 diabetes the likelihood of developing Alzheimer's disease increases by more than 50%. The purpose of the analysis proposed in this paper is to identify visually the correlation between Alzheimer's disease and type 2 diabetes and determine whether Alzheimer's disease is a form of brain diabetes mellitus. A dataset containing genomic microarray data relating to the two diseases is used for the analysis. First, we conduct an exploratory analysis using clustering techniques to perform a first screening of the samples and divide them into two different clusters. Then, we propose a predictive model for the classification and identify the genes equally expressed in the two types of samples. This makes it possible to select genes with significant values for the research in progress, on which pathway analysis must be performed to identify the classes they belong to. We also study the gene expression alterations of genes belonging to a specific pathway to determine if the differential expression is statistically significant. We provide a visual representation of connections in the pathways of both the diseases. Results indicate that there is a set of genes of significant importance for both type 2 diabetes and Alzheimer's disease, but that there is also a significant correlation with other neurodegenerative diseases. Consequently, it is possible to define the Alzheimer's disease as a form of cerebral diabetes mellitus.

Acute Leukemia is classified in terms of two distinct classes: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). This paper aims at defining a feature selection analysis process mainly based on Deep Learning for classifying the acute leukemia type. The considered dataset consists in data of patients affected by both the leukemia types. Both the leukemia types are characterized by a list of identical genes for all the patients. The analysis exploits feature selection techniques for reducing the consistent number of variables (genes). To this aim, we use linear models for differential expression for microarray data, and an autoencoder based unsupervised deep learning model to simplify and speed up the classification. Then, classification models have been implemented with the use of a deep neural network (DNN), obtaining an accuracy of approximately 92%. Moreover, the results have been compared with the ones provided by an approach based on support vector machines (SVM), giving an accuracy of 87,39%. Another feature selection approach based on genetic algorithms has been experimented, with worse performances. We also conducted a gene enrichment analysis based on the functional annotation of the differentially expressed genes. As a result, a differentially expressed pathway between the two pathologies has been detected.

BACKGROUND: SNARE proteins play an important role in different biological functions. This study aims to investigate the contribution of a new class of molecular descriptors (called SNARER) related to the chemical-physical properties of proteins in order to evaluate the performance of binary classifiers for SNARE proteins. RESULTS: We constructed a SNARE proteins balanced dataset, D128, and an unbalanced one, DUNI, on which we tested and compared the performance of the new descriptors presented here in combination with the feature sets (GAAC, CTDT, CKSAAP and 188D) already present in the literature. The machine learning algorithms used were Random Forest, k-Nearest Neighbors and AdaBoost and oversampling and subsampling techniques were applied to the unbalanced dataset. The addition of the SNARER descriptors increases the precision for all considered ML algorithms. In particular, on the unbalanced DUNI dataset the accuracy increases in parallel with the increase in sensitivity while on the balanced dataset D128 the accuracy increases compared to the counterpart without the addition of SNARER descriptors, with a strong improvement in specificity. Our best result is the combination of our descriptors SNARER with CKSAAP feature on the dataset D128 with 92.3% of accuracy, 90.1% for sensitivity and 95% for specificity with the RF algorithm. CONCLUSIONS: The performed analysis has shown how the introduction of molecular descriptors linked to the chemical-physical and structural characteristics of the proteins can improve the classification performance. Additionally, it was pointed out that performance can change based on using a balanced or unbalanced dataset. The balanced nature of training can significantly improve forecast accuracy.

In the era of digitized images, the goal is to extract information from them and create new knowledge thanks to Computer Vision techniques, Machine Learning and Deep Learning. This enables the use of images for early diagnosis and subsequent treatment of a wide range of diseases. In the dermatological field, deep neural networks are used to distinguish between melanoma and non-melanoma images. In this paper, we have underlined two essential points of melanoma detection research. The first aspect considered is how even a simple modification of the parameters in the dataset determines a change of the accuracy of classifiers. In this case, we investigated the Transfer Learning issues. Following the results of this first analysis, we suggest that continuous training-test iterations are needed to provide robust prediction models. The second point is the need to have a more flexible system architecture that can handle changes in the training datasets. In this context, we proposed the development and implementation of a hybrid architecture based on Cloud, Fog and Edge Computing to provide a Melanoma Detection service based on clinical and dermoscopic images. At the same time, this architecture must deal with the amount of data to be analyzed by reducing the running time of the continuous retrain. This fact has been highlighted with experiments carried out on a single machine and different distribution systems, showing how a distributed approach guarantees output achievement in a much more sufficient time.

The paper focuses on Cross-Version Defect Prediction (CVDP) where the classification model is trained on information of the prior version and then tested to predict defects in the components of the last release. To avoid the distribution differences which could negatively impact the performances of machine learning based model, we consider Dissimilarity-based Sparse Subset Selection (DS3) technique for selecting meaningful representatives to be included in the training set. Furthermore, we employ a Convolutional Neural Network (CNN) to generate structural and semantic features to be merged with the traditional software measures to obtain a more comprehensive list of predictors. To evaluate the usefulness of our proposal for the CVDP scenario, we perform an empirical study on a total of 20 cross-version pairs from 10 different software projects. To build prediction models we consider Logistic Regression (LR) and Random Forest (RF) and we adopt 3 evaluation criteria (i.e., F-measure, G-mean, Balance) to assess the prediction accuracy. Our results show that the use of CNN with both LR and RF models has a significant impact, with an improvement of ∼20% for each evaluation criteria. Differently, we notice that DS3does not impact significantly in improving prediction accuracy.

In recent years, Deep Learning techniques have achieved some success in bioinformatics tasks, including protein conformation prediction. In this work, we propose a Bidirectional Long Short-Term Memory (BLSTM) network system, called Human Proteins Angles Prediction (HPAP), in order to improve the prediction of dihedral angles of proteins. We have introduced a discrete subdivision in classes of 5° for protein torsion angles and four new features related to accessible surface area and volume. In total there are 73 classes (72 classes include the angles between -180° and 180°, a further class is used to code the free angles at the beginning of the sequence) with a maximum expected error of ±2.5°. We have tested three model variants in several parameter combinations. With our model, we have obtained a decrease of the mean absolute error of about 2° for the $\psi$ angle. Although our dataset is reduced in size, the accuracy of $\varphi$ and $\psi$ angles is comparable to the existing methods. Predicting angles accurately is useful for accurately reconstructing the three-dimensional structure of a protein. In this context, the prediction is limited to the $\varphi$ and $\psi$ angles and we will visualize what happens locally when a prediction is correct. In case the prediction is far from true angles, even a small error can deconstruct the backbone.

Autism Spectrum Disorder (ASD) is a term used to describe a constellation of early-onset social communication deficits and repetitive sensorimotor behaviours associated with a strong genetic component as well as other causes. This paper aims at creating a tool for automatically isolating segments of the speech useful for extract prosody features for identifying children with ASD. In particular, in this first phase of the research, we are interested in the creation of a large dataset of ’a’ vowels of ASD and not ASD people. The ’a’ vowel contains relevant information on the voice quality and emotional states. The proposed methodology is divided into 2 phases. In the former the input audio is analyzed to determine the vowel onset and offset points, useful to extract the vowel regions. Then a spectrogram graphically visualizing the identified vowels is provided as input to the second phase, where a convolutional neural network classifies whether the histogram represents the vowel ’a’. The convolutional network reaches an average accuracy of 95.00% (standard deviation ± 2.60%) on a dataset of 640 samples with Stratified 5-Fold Cross-Validation.

This paper exploits further uses of NLD (Normalized Levenshtein Distance), proposed in a recent study, to quantify the level of confusion of variables with the aim of verifying if they can provide indications about the presence of faults.We provide further evidence that fault prediction models based on the considered NLD measures can provide accurate estimations.

The Therapeutic Alliance (TA) between patient and health provider (therapist or clinician) is one of the most relevant factors for the success of a therapy. In the case of people suffering from Autism Spectrum Disorder (ASD), the alliance is extended to all the people involved in their care (i.e., teachers, therapists, clinicians, relatives). In this paper, we propose a multimedia application named Thea for empowering the TA of children with ASD by improving the communication among the TA members, sharing guidelines, multimedia contents, and strategies to comply with challenging behaviors and progress with particular attention towards end-users who are occasional smart-users. A detailed process for empowering the TA members by enhancing the informed interaction among all of them is proposed and implemented. A vocal assistant also supports patients/caregivers and therapists in documenting their activity with the person with ASD by recording videos in a free-hand modality. After a contextual analysis based on Thematic Analysis Template, Thea has been implemented using a user-centered development approach. We performed three iterations involving the end-users. A user study is performed at the third iteration. Results of the user study revealed a positive attitude towards the application. In particular, the perception of empowerment of participants increased after the tool had been used. We also highlighted the guidelines and tools that may be adopted for empowering different kinds of patients. The first results seem to suggest that the use of Thea may increase the belief of the caregivers of a person with ASD to be able to better take care of her, in a more controlled and informed way.

The use of smartphones is dramatically increasing. As a consequence, many organizations have the need of migrating their Java desktop applications towards the mobile technology. In this paper we present a miniaturization approach (process and supporting tool) named miniJava for the automatic miniaturization of Java desktop applications towards Android. The Java business logic is unvaried, while the calls to the Java objects of the interface are mapped into call to objects of the target technology. Semi-automatic layout fragmentation enables us to partition a desktop Java interface in various mobile screens. The approach also migrates the application files and enables the network connection. We conduct a user study where we assess the user perception in terms of user experience and affective reaction of the miniaturized application generated by a real Java desktop application which also has real Android variant. The end-user sample consisted of 18 participants. Results of this preliminary evaluation are encouraging: they do not reveal particular problems when using the miniaturized version automatically generated of the real desktop app with respect to its original Android variant, except for the novelty, which is better perceived for the native Android one.

Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the preprocessing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.

Over the last few years, biometrics has emerged as an increasingly reliable solution to recognize people using their physiological or behavioural characteristics. Despite their advantages, biometric systems raise many practical, ethical and legal issues. While, understandably, main concerns involve privacy and the risk of covert surveillance, profiling, and social control, another relevant question is the potential exclusion of individuals that, due to injuries, disability or genetic defects, may not meet the physical requirements used for the identification. In such situations, the risk comes out from the limits of current biometrics systems, which could exclude entire classes of individuals with negative spillovers on the possibility of access services and even exercise rights. In this paper, we focus on the recognition of iris suffering from Coloboma, a congenital abnormality of membranes of the eye. We first show how this pathological state impacts on the performance of the Daugman's algorithm, which represents the most widespread method used for the iris localization step in eye-based biometrics. Second, we designed and tested a classifier based on Convolutional Neural Network able to detect the presence of Coloboma with 95.45% accuracy. This result opens up new perspectives towards the definition of more sophisticated "diversity-aware" biometric systems.

Melanoma is the deadliest form of skin cancer. It mainly requires a visual diagnosis by dermatologists. However, a dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose correctly it. To this aim, in the last twenty years, Computer-Aided Diagnosis systems based on artificial vision are increasingly adopted to support dermatologists in the early diagnosis of melanoma. However, these systems exploits only a reduced set of parameters or they implement a melanoma classifier that tries to substitute the dermatologists, without supporting their experience in the classification of skin lesions. This paper proposes a mobile application for supporting the clinician decision in the diagnosis of melanoma directly in the dermatologist environment by using Augmented Reality technology. In particular, computer-generated perceptual information is added to the image of patient skin reporting the values of various parameters and the lesion classification based on deep learning approach for analyzing skin lesions and identifying melanoma.

Abstract Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.

Dyscalculia should be detected in the third primary class, but in many cases this disturb is diagnosed in later ages. In this paper we present a tool for supporting the clinician in the generation of responsive web-based tests for individuating people with disorder in basic numerical and arithmetic skills. Tests are created by combining specific kinds of questions which are delivered to a sample of people belonging to a specific target. The tool provides also support in the data analysis and in setting the alert thresholds for the selected user target. Results may be graphically visualized in summarized and single user way. Once the setting process is terminated, the test may be adopted. A case study on adult people is also presented.

In recent years, the integration of researches in Computer Science and medical fields has made available to the scientific community an enormous amount of data, stored in databases. In this paper, we analyze the data available in the Parkinson's Progression Markers Initiative (PPMI), a comprehensive observational, multi-center study designed to identify progression biomarkers important for better treatments for Parkinson's disease. The data of PPMI participants are collected through a comprehensive battery of tests and assessments including Magnetic Resonance Imaging and DATscan imaging, collection of blood, cerebral spinal fluid, and urine samples, as well as cognitive and motor evaluations. To this aim, we propose a technique to identify a correlation between the biomedical data in the PPMI dataset for verifying the consistency of medical reports formulated during the visits and allow to correctly categorize the various patients. To correlate the information of each patient's medical report, Information Retrieval techniques have been adopted, including the Latent Semantic Analysis technique suitable for constructing a concept space on patient information. Then, patients are grouped and classified into affected or not by using clustering algorithms according to the similarity of medical reports projected in the concept space. Results revealed that the proposed technique reached 95% of effectiveness in the classification of patients.

This paper investigates howimplementation variantsof design patterns impact on the retrieval effectiveness of a design pattern recovery tool. Specifically, we first defined several implementation variants of Adapter and Observer design patterns, by introducing constraints or relaxations on their canonical form. Then, we analyze the relationship between the complexity of these definitions and the precision and time needed by a design pattern recovery process we proposed in the past. To this end, we apply ePAD, an Eclipse plug-in for design pattern recovery, to eight software systems. We show that there exist interesting issues about the relationship between the complexity of the defined variants and the precision and time needed to recover their instances.

In this paper we present the Hunting Game Generator (HGG) system, a tool and a methodology for supporting geolocalized learning activities in Augmented Reality modality. The tool enables the teacher to create treasure hunting games directly on the mobile device. The teacher defines also the quizzes and advancing mechanism of the game. In this study we also conducted a qualitative investigation in terms of a focus group involving secondary school students aiming at evaluating their viewpoint on the relevance of the support the tool provides in learning activities. Students appeared very motivated by the tool, which is seen as a relevant support to traditional lectures.

We present a method and tool (ePAD) for the detection of design pattern instances in source code. The approach combines static analysis, based on visual language parsing and model checking, and dynamic analysis, based on source code instrumentation. Visual language parsing and static source code analysis identify candidate instances satisfying the structural properties of design patterns. Successively, model checking statically verifies the behavioral aspects of the candidates recovered in the previous phase. We encode the sequence of messages characterizing the correct behaviour of a pattern as Linear Temporal Logic (LTL) formulae and the sequence diagram representing the possible interaction traces among the objects involved in the candidates as Promela specifications. The model checker SPIN verifies that candidates satisfy the LTL formulae. Dynamic analysis is then performed on the obtained candidates by instrumenting the source code and monitoring those instances at runtime through the execution of test cases automatically generated using a search-based approach. The effectiveness of ePAD has been evaluated by detecting instances of 12 creational and behavioral patterns from six publicly available systems. The results reveal that ePAD outperforms other approaches by recovering more actual instances. Furthermore, on average ePAD achieves better results in terms of correctness and completeness.

In this paper we analyze at the class level the quality of the software portions including classes participating in design patterns instances (DP classes) with respect to the remaining software portions (NoDP classes). The performed study is based on 10 software systems from which information about design pattern instances and CK (Chidamber and Kemerer) metrics were obtained by exploiting repositories of pattern instances and the tool Understand, respectively. The analysis revealed that the use of design patterns impacts on the quality of the software.

MetricAttitude [18] is a visualization approach implemented in an environment that provides a mental picture of an object-oriented software by means of polymetric views of classes. In this paper, we describe a qualitative investigation we have conducted with a focus group involving developers aiming at evaluating their viewpoint on the relevance of the support MetricAttitude provides to perform comprehension tasks on source code. This investigation also allowed us to gather information on the developers' opinion on the MetricAttitude features and its software visualization metaphors. The discussion was animated and participants provided a number of useful suggestions for improving the visualization. The tool was considered very useful, while some usability problems have to be addressed. Specifically, the information provided has to be further filtered to easier software comprehension tasks.

MetricAttitude is a visualization tool based on static analysis that provides a mental picture by viewing an object-oriented software by means of polymetric views. In this tool demonstration paper, we integrate an information retrieval engine in MetricAttitude and name this new version as MetricAttitude++. This new tool allows the software engineer to formulate free-form textual queries and shows results on the polymetric views. In particular, MetricAttitude++ shows on the visual representation of a subject software the elements that are more similar to that query. The navigation among elements of interest can be then driven by the polymetric views of the depicted elements and/or reformulating the query and applying customizable filters on the software view. Due to its peculiarities, MetricAttitude++ can be applicable to many kinds of software maintenance and evolution tasks (e.g., concept location and program comprehension).

We carried out a family of controlled experiments to investigate whether the use of abbreviated identifier names, with respect to full-word identifier names, affects fault fixing in C and Java source code. This family consists of an original (or baseline) controlled experiment and three replications. We involved 100 participants with different backgrounds and experiences in total. Overall results suggested that there is no difference in terms of effort, effectiveness, and efficiency to fix faults, when source code contains either only abbreviated or only full-word identifier names. We also conducted a qualitative study to understand the values, beliefs, and assumptions that inform and shape fault fixing when identifier names are either abbreviated or full-word. We involved in this qualitative study six professional developers with 1--3 years of work experience. A number of insights emerged from this qualitative study and can be considered a useful complement to the quantitative results from our family of experiments. One of the most interesting insights is that developers, when working on source code with abbreviated identifier names, adopt a more methodical approach to identify and fix faults by extending their focus point and only in a few cases do they expand abbreviated identifiers.

In this paper, we present LifeBook, a Personal Information Management (PIM) system that handles information on events captured by all the user's devices. Our PIM retrieves events on the basis of both the user's context and event similarity, which is computed by exploiting an information retrieval technique. We aggregated together the similarity of content, location, time, and event type to relate and surf the events. To this aim, we propose a re-find interface enabling the user to search and visualize information already seen before, of which he remembers some context aspects, such as time and/or place. The events captured on different devices are stored on the cloud without user intervention. A preliminary quantitative and qualitative evaluation has been also conducted to assess the effectiveness of LifeBook. Results in terms of time, effort and relevance of the information provided suggest that LifeBook be a viable means to retrieve personal information. Participants in the empirical investigation also considered the tool appropriate for supporting information re-finding tasks.

MetricAttitude is a visualization approach implemented in an environment that provides a mental picture by viewing an object-oriented software by means of polymetric views of classes (i.e., fine-grained). In this paper, we present an extension of MetricAttitude which visualizes a software by levels considering not only its class view but also its package views in terms of nested packages (i.e., coarse-grained). Packages are represented by using visual properties associated to Martin's metrics [15]. The new approach and its supporting visualization environment also allow showing relationships among packages.

We present an approach to enable end-users to graphically compose their own applications directly on their mobile phone, mainly integrating the functionalities available on the device and those provided by pervasive and Internet services. To this aim, we propose a methodology and a graphical notation enabling the user to compose mobile applications, named MicroApps: the user creates an application following an incremental and iterative development process; he composes icons representing (pervasive) services mainly by touch-based selection and following a data-flow approach. He is not in charge of the creation of the user interface, which is automatically generated. The methodology enables the end-user to develop applications and/or compose services on the smartphone, so paving the way towards new scenarios where smartphones replace and overtake the Personal Computer, given their native possibility of wide connectivity, when augmented by features for interaction with remote systems and sensors. The methodology has been evaluated through an empirical analysis that revealed that in spite of the reduced size of the screen the use of the MicroApp Generator tool improves the effectiveness in terms of time and editing errors with respect to the use of MIT App Inventor [1] .

The detection of behavioral design patterns is more accurate when a dynamic analysis is performed on the candidate instances identified statically. Such a dynamic analysis requires the monitoring of the candidate instances at run-time through the execution of a set of test cases. However, the definition of such test cases is a time-consuming task if performed manually, even more, when the number of candidate instances is high and they include many false positives. In this paper we present the results of an empirical study aiming at assessing the effectiveness of dynamic analysis based on automatically generated test cases in behavioral design pattern detection. The study considered three behavioral design patterns, namely State, Strategy, and Observer, and three publicly available software systems, namely JHotDraw 5.1, QuickUML 2001, and MapperXML 1.9.7. The results show that dynamic analysis based on automatically generated test cases improves the precision of design pattern detection tools based on static analysis only. As expected, this improvement in precision is achieved at the expenses of recall, so we also compared the results achieved with automatically generated test cases with the more expensive but also more accurate results achieved with manually built test cases. The results of this analysis allowed us to highlight costs and benefits of automating dynamic analysis for design pattern detection.

In this demonstration we present ePADevo, an Eclipse plug-in for recovering design pattern instances from object-oriented source code. The tool is able to recover design pattern instances through a static analysis performed on a data model extracted from source code, and a dynamic analysis performed through the instrumentation and the monitoring of the software system. Dynamic analysis is performed with automatically generated test cases exploiting the EvoSuite tool.

I have enhanced Metric Attitude. It is a visualization tool based on static analysis that provides a mental picture by viewing an object-oriented software system by means of polymetric views. In particular, we have integrated an Information Retrieval engine and named this new version of visualization tool as Metric Attitude++. It allows the user to formulate a textual query and to show on the visual representation of the subject software the elements that are more similar to that query. This could be useful in all those cases in which a user needs to identify (or to localize) features implemented in the source code. Several filters are also available to hide possibly irrelevant details and to ease the browsing and then the comprehension of a software system. Finally, we have applied Metric Attitude++ on a number of object-oriented software systems. In this paper, we report preliminary results of a quantitative study on a widely studied open-source software, namely JEdit. On the basis of our results it seems that Metric Attitude++ can be effectively applied to different kinds of source code comprehension tasks and to concept location in source code, in particular.

Design patterns are recognized as a means to improve software maintenance by furnishing an explicit specification of class and object interactions and their underlying intent [Gamma et al. 1995]. Only a few empirical investigations have been conducted to assess whether the kind of documentation for design patterns implemented in source code affects its comprehensibility. To investigate this aspect, we conducted a family of four controlled experiments with 88 participants having different experience (i.e., professionals and Bachelor, Master, and PhD students). In each experiment, the participants were divided into three groups and asked to comprehend a nontrivial chunk of an open-source software system. Depending on the group, each participant was, or was not, provided with graphical or textual representations of the design patterns implemented within the source code. We graphically documented design-pattern instances with UML class diagrams. Textually documented instances are directly reported source code as comments. Our results indicate that documenting design-pattern instances yields an improvement in correctness of understanding source code for those participants with an adequate level of experience.

Context: abbreviated and full-word identifier names in dealing with faults in source code. Goal: investigating whether the use of abbreviated identifier names affects the ability of novice professional software developers in identifying and fixing faults in Java code. Method: external replication. Results: the results of the original experiment (conducted on C code) were confirmed. Conclusions: the difference in using abbreviated and full-word identifiers is not statistically significant with respect to the time to complete a task and the number of faults identified and fixed.

MetricAttitude is a visualization tool based on static analysis that provides a mental picture by viewing an object-oriented software system by means of polymetric views. In this paper, we present a preliminary empirical investigation based on a questionnaire-based survey to assess Metric Attitude with respect to source code comprehension tasks. Participants involved in this study were Computer Science students and software professionals. The results suggest that Metric Attitude is a viable means to comprehend source code and that both kinds of participants in the empirical investigation considered it to be appropriate in source code comprehension.

Websites built using Web Content Management Systems (WCMSs) usually provide their users with three types of access structures to surf their contents: indexes of categories, breadcrumb trails, and sitemaps. In addition, to find contents of his/her interest, a user can perform more or less advanced full-text searches. In this paper we propose an automatic approach to extend the navigation structure of websites developed using WCMSs with Semantic Navigation Maps (SNMs), a complementary navigation structure that enables linking and navigating contents based on their lexical similarity. The approach uses an information retrieval technique (namely, Latent Semantic Indexing) to identify lexical similarities between textual contents, and a fuzzy clustering algorithm to form groups of similar web pages. For each page of the website, a set of navigation links towards pages showing similar content and a measure of such similarity is provided. The paper presents the approach to generate SNMs, an implementation for the Joomla! open source WCMS, and the results of an empirical evaluation involving two real world websites built using this WCMS.

The visualization of information contained in reports is an important aspect of human-computer interaction, for both the accuracy and the complexity of relationships between data must be preserved. A greater attention has been paid to individual report visualization through different types of standard graphs (Histograms, Pies, etc.). However, this kind of representation provides separate information items and gives no support to visualize their relationships which are extremely important for most decision processes. This paper presents a design methodology exploiting the visual language CoDe based on a logic paradigm. CoDe allows to organize the visualization through the CoDe model which graphically represents relationships between information items and can be considered a conceptual map of the view. The proposed design methodology is composed of four phases: the CoDe Modeling and OLAP Operation pattern definition phases define the CoDe model and underlying metadata information, the OLAP Operation phase physically extracts data from a data warehouse and the Report Visualization phase generates the final visualization. Moreover, a case study on real data is provided.

We carried out a controlled experiment to investigate whether the use of abbreviated identifier names affects the ability of novice software developers to identify and fix faults in source code. The experiment was conducted with 49 students in Computer Science. The results of the statistical analyses indicate that there was not a significant difference to identify and to fix faults, when source code contains either abbreviated and full-word identifier names. In other words, it seems that abbreviated identifiers provide the same information as full-word identifiers on the solution domain and the implementation.

This paper presents an investigation, based on the combined use of two techniques: a questionnaire-based survey and an empirical analysis, to assess the effectiveness and efficacy of the MicroApp environment to support End-Users in the visual composition of their own applications directly on their mobile phone. The satisfaction of the End-Users has been investigated as well. The context of this study was constituted of students, administrative personnel and consultants of the University of Salerno. The survey shows a positive satisfaction degree of all the involved subjects, while the empirical analysis reveals that the use of the Micro App tool increases the efficiency and, in case of complex tasks, also the simplicity with respect to the use of a PC-based similar tool proposed by Google.

In this paper, we present a visualization approach for the reverse engineering of object-oriented (OO) software systems and its implementation in MetricAttitude, an Eclipse Rich Client Platform application. The goal of our proposal is to ease both the comprehension of a subject system and the identification of fault-prone classes. The approach graphically represents a suite of object-oriented design metrics (e.g., Weighted Methods per Class) and "traditional" code-size metrics (e.g., Lines Of Code). To assess the validity of MetricAttitude and its underlying approach, we have conducted a case study on the framework Eclipse 3.5. The study has provided indications about the tool scalability, interactivity, and completeness. The results also suggest that our proposal can be successfully used in the identification of fault-prone classes.

Abstract In this paper we present an approach to automate the architecture recovery process of software systems. The approach is built on information retrieval and clustering techniques, and, in particular, uses Latent Semantic Indexing (LSI) to get similarities among software entities (e.g., programs or classes) and the k-means clustering algorithm to form groups of software entities that implement similar functionality. In order to improve computational time in the context of the software evolution and then reduce energy waste, the architecture recovery process can be also applied by using fold-in and fold-out mechanisms that, respectively, add and remove software entities to the LSI representation of the understudy software system. The approach has been implemented in a prototype of a supporting software system as an Eclipse plug-in. Finally, to assess the approach and the plug-in, we have conducted an empirical investigation on five open source software systems implemented using the programming languages Java and C/C++. In the investigation special emphasis has been also given to the effect of using the fold-in and fold-out mechanisms.

We propose an approach that leverages lexical information and fuzzy clustering to reduce the number of the design pattern instances that existing approaches based on structural information (i.e., navigating the dependencies among software elements) erroneously recover in source code. To assess the effectiveness of the techniques, we present the results of a case study conducted on four open source software systems implemented in java. The data analysis indicates that the use of lexical information and fuzzy clustering improves the correctness of the results achieved by existing design pattern recovery approaches based on structural information, while preserving the number of design pattern instances correctly identified.

In this paper we present the preliminary results of a controlled experiment to assess the contribution provided by the design patterns on the maintenance of source code. In particular, the study aimed at assessing the effort and the efficiency to perform maintenance operations in case design pattern instances are properly documented and provided to the maintainer. The context of the experiment is constituted of Master Students in Computer Science at the University of Basilicata. The preliminary analysis conducted on the gathered data revealed that the effort is significantly reduced in case design pattern instances are properly documented and provided to the subjects. Similarly, the efficiency is significantly better in case the documentation of design pattern instances is used to accomplish maintenance operations.

In this paper we present a recognizer of sketched symbols based on Latent-Dynamic Conditional Random Fields (LDCRF), a discriminative model for sequence classification. The LDCRF model classifies unsegmented sequences of strokes into domain symbols by taking into account contextual and temporal information. In particular, LDCRFs learn the extrinsic dynamics among strokes by modeling a continuous stream of symbol labels, and learn internal stroke sub-structure by using intermediate hidden states. The performance of our work is evaluated in the electric circuit domain.

The extraction of design pattern information from software systems can provide conspicuous insight to software engineers on the software structure and its internal characteristics. In this demonstration we present ePAD, an Eclipse plug-in for recovering design pattern instances from object-oriented source code. The tool is able to recover design pattern instances through a structural analysis performed on a data model extracted from source code, and a behavioral analysis performed through the instrumentation and the monitoring of the software system. ePAD is fully configurable since it allows software engineers to customize the design pattern recovery rules and the layout used for the visualization of the recovered instances.

A number of clustering based approaches and tools have been proposed in the past to partition a software system into subsystems. The greater part of these approaches is semiautomatic, thus requiring human decision to identify the best partition of software entities into clusters among the possible partitions. In addition, some approaches are conceived for software systems implemented using a particular programming language (e.g., C and C++). In this paper we present an approach to automate the partitioning of a given software system into subsystems. In particular, the approach first analyzes the software entities (e.g., programs or classes) and then using Latent Semantic Indexing the dissimilarity between these entities is computed. Finally, software entities are grouped using iteratively the k-means clustering algorithm. The approach has been implemented in a prototype of a supporting software system as an Eclipse plug-in. Finally, to assess the approach and the plug-in, we have conducted an empirical investigation on three open source software systems implemented using the programming languages Java and C/C++.

Sketching is considered as a way to naturally express ideas during the early phases of design. For this reason, many efforts have been made to develop user interfaces and recognizers, which enable users to create sketches using pen-based devices. However, in some domains, such as in architectural and engineering fields, the drawing process turns out to be particularly tedious and time-consuming, since the symbols to be drawn may have a complex shape and recur many times in the sketches. In this paper we present a technique for symbol completion that allows users to rapidly draw diagrammatic sketches. The completion technique recovers the information on missing strokes by interacting with symbol recognizers, which are automatically generated from grammar specifications. Moreover, in order to maintain the sketch layout more familiar to the users, the added strokes are drawn according to the user drawing style.

The existing sketch recognizers perform only a limited drawing recognition since they process simple sketches, or rely on drawing style assumptions that reduce the recognition complexity, and in most cases they require a substantial amount of training data. In this paper we present a parsing strategy for the recognition of hand-drawn diagrams that can be used in interactive sketch interfaces. The approach is based on grammar formalism, namely sketch grammars (SkGs), for describing both the symbols' shape and the syntax of diagrammatic notations, and from which recognizers are automatically generated. The recognition system was evaluated in the domain of UML use case diagrams and the results highlight the recognition accuracy improvements produced by the use of context in the disambiguation process

Sketch-based user interfaces are increasingly common and are being built for a variety of different disciplines. However, at present the implementation of sketch recognizers is quite time consuming since they are mostly based on specific techniques, as opposed to several other fields such as textual/visual languages and speech recognition, which benefit from the availability of compiler generation techniques and tools. This paper proposes a grammar formalism, namely Sketch Grammars (SkGs), for describing both the shape of the symbols' language and the syntax of sketch languages. Recognizers are automatically generated from the sketch grammar descriptions.

In this paper we propose an approach for constructing sketch parsers whose recognition accuracy and speed is significantly improved by acquiring information on the user's sketching style during a training phase. The construction process consists in specifying a sketch grammar description of the language syntax, automatically generating a parser from such specification, and let the user train the recognition system on a set of sketch sentences.

Several disciplines require the support of computer-based tools for creating sketches during early design phases. Unfortunately, most computer programs cannot parse and semantically interpret handwritten sketches. In this paper, we present a framework for modeling sketch languages and for generating parsers to recognize them. The underlying parsing technique addresses the issues of stroke clustering and ambiguity resolution in sketches. We also present a workbench supporting the presented framework

We present the Component-Based Visual Environment Development (CB-VED) process for building visual language environments and introduce the Visual Language Desk (VLDesk) system supporting its implementation. The proposed approach is based on software reuse at different granularity levels and enables incremental development. The VLDesk exploits all the knowledge gained from the development of the Visual Language Compiler-Compiler tool extending its functionalities with many adjunctive features useful in the presented development process. One of the aims of this research consists of the application of software engineering techniques to the incremental development of visual language environments.