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.

Structure-Property Relationships and Charge Transport Modeling of Organic Molecular Materials

From the perspective of a new-generation opto-electronic technology based on organic semiconductors, a major objective is to achieve a deep and detailed knowledge of the structure-property relationships, in order to optimize the electronic, optical, and charge transport properties by tuning the chemical-physical characteristics of the compounds. The purpose of this dissertation is to contribute to such understanding, through suitable theoretical and computational studies. Precisely, the structural, electronic, optical, and charge transport characteristics of several promising organic materials recently synthesized are investigated by means of an integrated approach encompassing quantum-chemical calculations, molecular dynamics and kinetic Monte Carlo simulations. Particular care is addressed to the rationalization of optical and charge transport properties in terms of both intra- and intermolecular features. Moreover, a considerable part of this project involves the development of a home-made set of procedures and parts of software code required to assist the modeling of charge transport properties in the framework of the non-adiabatic hopping mechanism applied to organic crystalline materials. As a first part of my investigations, I mainly discuss the optical, electronic, and structural properties of several core-extended rylene derivatives, which can be regarded to as model compounds for graphene nanoribbons. Two families have been studied, consisting in bay-linked perylene bisimide oligomers and N-annulated rylenes. Beside rylene derivatives, my studies also concerned electronic and spectroscopic properties of tetracene diimides, quinoidal oligothiophenes, and oxygen doped picene. As an example of device application, I studied the structural characteristics governing the efficiency of resistive molecular memories based on a derivative of benzoquinone. Finally, as a second part of my investigations, I concentrate on the charge transport properties of perylene bisimides derivatives. Precisely, a comprehensive study of the structural and thermal effects on the charge transport of several core-twisted chlorinated and fluoro-alkylated perylene bisimide n-type semiconductors is presented.

Lipophilic nucleosides: studies toward self-assembled functional materials

Molecular self-assembly takes advantage of supramolecular non-covalent interactions (ionic, hydrophobic, van der Waals, hydrogen and coordination bonds) for the construction of organized and tunable systems. In this field, lipophilic guanosines can represent powerful building blocks thanks to their aggregation proprieties in organic solvents, which can be controlled by addition or removal of cations. For example, potassium ion can template the formation of piled G-quartets structures, while in its absence ribbon-like G aggregates are generated in solution. In this thesis we explored the possibility of using guanosines as scaffolds to direct the construction of ordered and self-assembled architectures, one of the main goals of bottom-up approach in nanotechnology. In Chapter III we will describe Langmuir-Blodgett films obtained from guanosines and other lipophilic nucleosides, revealing the “special” behavior of guanine in comparison with the other nucleobases. In Chapter IV we will report the synthesis of several thiophene-functionalized guanosines and the studies towards their possible use in organic electronics: the pre-programmed organization of terthiophene residues in ribbon aggregates could allow charge conduction through π-π stacked oligothiophene functionalities. The construction and the behavior of some simple electronic nanodevices based on these organized thiopehene-guanosine hybrids has been explored.

Self-Assembled Monolayers (SAMs) in Organic Field-Effect Transistors

Organic printed electronics is attracting an ever-growing interest in the last decades because of its impressive breakthroughs concerning the chemical design of π-conjugated materials and their processing. This has an impact on novel applications, such as flexible-large-area displays, low- cost printable circuits, plastic solar cells and lab-on-a-chip devices. The organic field-effect transistor (OFET) relies on a thin film of organic semiconductor that bridges source and drain electrodes. Since its first discovery in the 80s, intensive research activities were deployed in order to control the chemico-physical properties of these electronic devices and consequently their charge. Self-assembled monolayers (SAMs) are a versatile tool for tuning the properties of metallic, semi-conducting, and insulating surfaces. Within this context, OFETs represent reliable instruments for measuring the electrical properties of the SAMs in a Metal/SAM/OS junction. Our experimental approach, named Charge Injection Organic-Gauge (CIOG), uses OTFT in a charge-injection controlled regime. The CIOG sensitivity has been extensively demonstrated on different homologous self-assembling molecules that differ in either chain length or in anchor/terminal group. One of the latest applications of organic electronics is the so-called “bio-electronics” that makes use of electronic devices to encompass interests of the medical science, such as biosensors, biotransducers etc… As a result, thee second part of this thesis deals with the realization of an electronic transducer based on an Organic Field-Effect Transistor operating in aqueous media. Here, the conventional bottom gate/bottom contact configuration is replaced by top gate architecture with the electrolyte that ensures electrical contact between the top gold electrode and the semiconductor layer. This configuration is named Electrolyte-Gated Field-Effect Transistor (EGOFET). The functionalization of the top electrode is the sensing core of the device allowing the detection of dopamine as well as of protein biomarkers with ultra-low sensitivity.