Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

Sviluppo di sperimentazioni per la produzione di getti in lega di alluminio a caratteristiche difettologiche e microstrutturali controllate, per la validazione di modelli di previsione delle porosità

Nei processi di progettazione e produzione tramite tecnologie di colata di componenti in alluminio ad elevate prestazioni, risulta fondamentale poter prevedere la presenza e la quantità di difetti correlabili a design non corretti e a determinate condizioni di processo. Fra le difettologie più comuni di un getto in alluminio, le porosità con dimensioni di decine o centinaia di m, note come microporosità, hanno un impatto estremamente negativo sulle caratteristiche meccaniche, sia statiche che a fatica. In questo lavoro, dopo un’adeguata analisi bibliografica, sono state progettate e messe a punto attrezzature e procedure sperimentali che permettessero la produzione di materiale a difettologia e microstruttura differenziata, a partire da condizioni di processo note ed accuratamente misurabili, che riproducessero la variabilità delle stesse nell’ambito della reale produzione di componenti fusi. Tutte le attività di progettazione delle sperimentazioni, sono state coadiuvate dall’ausilio di software di simulazione del processo fusorio che hanno a loro volta beneficiato di tarature e validazioni sperimentali ad hoc. L’apparato sperimentale ha dimostrato la propria efficacia nella produzione di materiale a microstruttura e difettologia differenziata, in maniera robusta e ripetibile. Utilizzando i risultati sperimentali ottenuti, si è svolta la validazione di un modello numerico di previsione delle porosità da ritiro e gas, ritenuto ad oggi allo stato dell’arte e già implementato in alcuni codici commerciali di simulazione del processo fusorio. I risultati numerici e sperimentali, una volta comparati, hanno evidenziato una buona accuratezza del modello numerico nella previsione delle difettologie sia in termini di ordini di grandezza che di gradienti della porosità nei getti realizzati.

Subject-specific musculoskeletal models of the lower limbs for the prediction of skeletal loads during motion

The determination of skeletal loading conditions in vivo and their relationship to the health of bone tissues, remain an open question. Computational modeling of the musculoskeletal system is the only practicable method providing a valuable approach to muscle and joint loading analyses, although crucial shortcomings limit the translation process of computational methods into the orthopedic and neurological practice. A growing attention focused on subject-specific modeling, particularly when pathological musculoskeletal conditions need to be studied. Nevertheless, subject-specific data cannot be always collected in the research and clinical practice, and there is a lack of efficient methods and frameworks for building models and incorporating them in simulations of motion. The overall aim of the present PhD thesis was to introduce improvements to the state-of-the-art musculoskeletal modeling for the prediction of physiological muscle and joint loads during motion. A threefold goal was articulated as follows: (i) develop state-of-the art subject-specific models and analyze skeletal load predictions; (ii) analyze the sensitivity of model predictions to relevant musculotendon model parameters and kinematic uncertainties; (iii) design an efficient software framework simplifying the effort-intensive phases of subject-specific modeling pre-processing. The first goal underlined the relevance of subject-specific musculoskeletal modeling to determine physiological skeletal loads during gait, corroborating the choice of full subject-specific modeling for the analyses of pathological conditions. The second goal characterized the sensitivity of skeletal load predictions to major musculotendon parameters and kinematic uncertainties, and robust probabilistic methods were applied for methodological and clinical purposes. The last goal created an efficient software framework for subject-specific modeling and simulation, which is practical, user friendly and effort effective. Future research development aims at the implementation of more accurate models describing lower-limb joint mechanics and musculotendon paths, and the assessment of an overall scenario of the crucial model parameters affecting the skeletal load predictions through probabilistic modeling.