Notions d'éthique dans la brevetabilité des inventions : une étude de droit comparé

Le droit des brevets a pour but premier de favoriser les développements technologiques et industriels. Cependant, à l’heure actuelle, ce domaine voit son rôle confronté à une forme de crise, à cause plus précisément des avancées constatées dans le secteur des biotechnologies. De difficiles questions fondamentales se posent à différents niveaux, que ce soit socialement, moralement et légalement. Au vu de ces observations, la question est de savoir, dans le cadre de cette étude, si la régulation devrait être plus signifiante en tenant compte des considérations morales et éthiques dans le processus de brevetabilité. Cette étude a donc pour but de comparer et d’évaluer les diverses solutions proposées en Europe, aux États-Unis et au Canada, afin de déterminer quelle serait la voie envisageable vers la résolution de cette problématique. Par exemple, dans ce contexte, on peut pointer l’approche européenne, où la CBE et la Directive du Parlement européen relative à la protection des inventions biotechnologiques (98/44/CE) semblent introduire des notions éthiques dans leurs textes juridiques. Un tel procédé apporte des notions qui peuvent être considérées comme vagues et évolutives dans un processus qui se veut apparemment technique. Alors que si l’on prend l’approche nord-américaine, celle-ci se fonde sur des critères de brevetabilité dénués de toutes considérations morales. Par l’analyse de ces éléments, une voie possible pourrait être décrite vers un système des brevets qui répondrait mieux aux exigences actuelles.Patent law has for primary goals to promote new tehnological and industrial developments. However, patent law has been currently confronted to some questioning about its role raised particulary by the new advancement made in biotechnologies. Difficult fondamental questions must be addressed at different levels: socialy, moraly and legaly. Following these obersvations, the question to answer, in this study, is whether regulation should be more significant by taking into account some moral and ethical considerations in the process of patentability. The goal of this study is to compare and estimate the various solutions provided by Europe, the USA and Canada, to determine what could be the answer of that problematic. For exemple, in this context, we can point out the European approach, where the EPC and the Directive of the European Parliamment on the Legal Protection of Biotechnological Inventions (98/44/CE) seem to introduce ethical notions in their legislation. Such approach brings elements which can be discribed as indistinct and progressive in a process apparently defined as technical. But if we take the North American approach, its patentability critea are not based on any moral consideration. So by analysing these elements, an approach could be defined to a more appropriate patent legislation fitting the actual necessities

A pilot study to determine the normal haematological indices for young Malawian adults in Blantyre, Malawi

Background Reference ranges for haematological and other laboratory tests in most African countries are based on populations in Europe and America and, because of environmental and genetic factors, these may not accurately reflect the normal reference ranges in African populations.Aim To determine the distribution of haematological parameters in healthy individuals residing in Blantyre, Malawi. We also examined the effect of sociodemographic and nutritional factors on the haematological variables.Methods We conducted a proof-of-concept cross-sectional study, involving 105 healthy blood donors at Malawi Blood Transfusion Service in Blantyre. Eligible participants were HIV-negative males and females, aged 19 to 35 years, who did not have any evidence of acute or chronic illness, or bloodborne infection. We performed the haematological tests at the Malawi- Liverpool Wellcome Trust laboratory in Blantyre, and the screening tests at Malawi Blood Transfusion Service laboratories.Results Out of 170 consenting healthy volunteers, haematological results were available for 105 participants. The proportions of results which were below the lower limit of the manufacturer’s reference ranges were 35.2% (37/105) for haemoglobin, 15.2% (16/105) for neutrophils, 23.8% (25/105) for eosinophils, and 88.6 % (93/105) for basophils. The proportions of results that were above the upper limit of the manufacturer’s reference ranges were 9.5% (10/105) for platelets and 12.4% (13/105) for monocytes. We also observed that the mean leucocyte and basophil counts were significantly higher in males than females (p = 0.042 and p = 0.015, respectively). There were no statistically significant differences in haematological results observed among different ethnic, age, and body mass index groups.Conclusions Over half of otherwise healthy study participants had at least one abnormal haematological result, using previously established foreign standards. More detailed studies are needed to establish locally relevant normal ranges for different age groups and other demographic characteristics of the Malawian population. This will lead to accurate interpretation of laboratory results

A qualitative exploration of roles and expectations of male partners from PMTCT services in rural Malawi

BackgroundPrevention of mother-to-child transmission of HIV (PMTCT) is effective in curbing rates of HIV infection in children because its interventions reduce the rates of transmission during pregnancy, in labour, and in breastfeeding. Male involvement (MI) greatly influences uptake and adherence to PMTCT services. Lack of clarity on the roles and expectations of men in PMTCT is one of the main barriers to MI. The main aim of the study was to explore the roles and expectations of male partners from PMTCT services in Malawi.MethodsThis was a descriptive qualitative study that involved men whose partners were either pregnant or breastfeeding a child, health care workers working in PMTCT services for over six months, and traditional leaders. We conducted 9 in-depth interviews and 12 key informant interviews from January to March 2018. All interviews were audio-recorded, transcribed, and translated. Thematic analysis was employed to analyze data.ResultsThe subjective and community norms and attitudes of men towards PMTCT provide the context in which male partners define the specific roles they render and the services they expect from PMTCT services. The roles of men in PMTCT service were contextualized in what is socially acceptable and normalized in the setting and include supportive roles expressed as accompanying the wife to attend; antenatal care services, Dry blood sample collection (DBS) when its due, keeping appointments when is due to take the ARVs, providing financial support; HIV prevention behavior change and decision-making roles. The desired services within PMTCT include health assessment such as checking their weight; blood pressure; blood sugar and promotion activities such as education sessions that are provided in a male-friendly manner that is in tandem with existing socio-cultural norms and attitudes of men towards such services.ConclusionThe roles of male partners in PMTCT services are underpinned by subjective norms and what is socially acceptable within a specific context. The services that men require from PMTCT services are influenced by their attitudes and beliefs towards PMTCT interventions. Services should be male-tailored provided in an atmosphere that allows and accepts male partners to exercise their roles in PMTCT services

ERAS® protocol improves survival after radical cystectomy: A single-center cohort study.

To evaluate Enhanced recovery after surgery (ERAS®) protocol on oncological outcomes for patients treated with radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB). A prospectively maintained single-institutional database comprising 160 consecutive UCB patients who underwent open RC from 2012 to 2020 was analyzed. Patients receiving chemotherapy and those with a urinary diversion other than ileal conduit were excluded. Patients were divided into two groups according to the perioperative management (ERAS® and pre-ERAS®). The study aimed to evaluate the impact of the ERAS® protocol on survival at five years after surgery using a Kaplan-Meier log-rank test. A multivariable Cox proportional hazards model was used to identify prognostic factors for cancer-specific (CSS) and overall survival (OS). Of the 107 patients considered for the final analysis, 74 (69%) were included in the ERAS® group. Median follow-up for patients alive at last follow-up was 28 months (interquartile range [IQR] 12-48). Five-years CSS rate was 74% for ERAS® patients, compared to 48% for the control population (P = 0.02), while 5-years OS was 31% higher in the ERAS® (67% vs. 36%, P = .003). In the multivariable analysis, ERAS® protocol and tumor stage were independent factors of CSS, while ERAS®, tumor stage so as total blood loss were independent factors for OS. A dedicated ERAS® protocol for UCB patients treated with RC has a significant impact on survival. Reduction of stress after a major surgery and its potential improvement of perioperative patient's immunity may explain these data

Using routinely collected blood donation data for expanded HIV and syphilis surveillance in Blantyre District, Malawi

BACKGROUND: WHO recommends all blood donations be screened for transfusion transmissible infections. However, these data are not incorporated into national surveillance systems in Malawi. We set out to use routinely collected data from blood donors in Blantyre district, Malawi, an area of high HIV and syphilis prevalence, to explore current HIV and syphilis prevalence and identify recent sero-conversions among repeat donors. METHODS: We conducted a retrospective cohort analysis of blood donation data collected by the Malawi Blood Transfusion Service (MBTS) between October 1st 2015 and May 31st 2021. All blood donations were routinely screened for WHO-prioritized transfusion-transmissible infections, including HIV and syphilis. We characterized donor demographics as well as screening outcomes, including identifying sero-conversions among repeat donors who previously tested negative. Logistic regression was used to model the impact of individual level covariates on the probability of sero-conversion. RESULTS: A total of 93,199 donations from 5,054 donors were recorded, with 7 donors (0.1%) donating a maximum of 24 times. The majority of donors were male (4,294; 85%) and students (3264; 64.6%) at the time of their first donation. Of those screened for HIV and syphilis, 126 (2.5%, 126/5,049) and 245 (4.9%, 245/5,043) tested positive respectively.Among repeat donors who previously tested negative, 87 HIV sero-conversions and 195 syphilis sero-conversions were identified over the study period, indicating an HIV incidence rate of 6.86 per 1,000 person-years and a syphilis incidence rate of 15.37 per 1,000 person-years. Donors who were female or aged 16-19 at the time of first donation had a higher risk of HIV or syphilis sero-conversion. CONCLUSIONS: Routinely collected data from national blood donation services may be used to enhance existing population-level disease surveillance systems, particularly in high prevalence areas. While blood donors are generally considered a low-risk population for HIV and syphilis, we were able to identify and characterise blood donor populations at increased risk of sero-conversion over the study period. This information will provide insight into priority prevention areas in Blantyre district and help to inform targeted interventions for improved prevention, testing and treatment

Elevated Plasma Von Willebrand Factor and Propeptide Levels in Malawian Children with Malaria

In children with malaria plasma VWF and propeptide levels are markedly elevated in both cerebral and mild paediatric malaria, with levels matching disease severity, and these normalize upon recovery. High levels of both markers also occur in retinopathy-negative 'cerebral malaria' cases, many of whom are thought to be suffering from diseases other than malaria, indicating that further studies of these markers will be required to determine their sensitivity and specificity

Whole blood versus red cell concentrates for children with severe anaemia: a secondary analysis of the Transfusion and Treatment of African Children (TRACT) trial

Background The TRACT trial established the timing of transfusion in children with uncomplicated anaemia (haemoglobin 4–6 g/dL) and the optimal volume (20 vs 30 mL/kg whole blood or 10 vs 15 mL/kg red cell concentrates) for transfusion in children admitted to hospital with severe anaemia (haemoglobin <6 g/dL) on day 28 mortality (primary endpoint). Because data on the safety of blood components are scarce, we conducted a secondary analysis to examine the safety and efficacy of different pack types (whole blood vs red cell concentrates) on clinical outcomes. Methods This study is a secondary analysis of the TRACT trial data restricted to those who received an immediate transfusion (using whole blood or red cell concentrates). TRACT was an open-label, multicentre, factorial, randomised trial conducted in three hospitals in Uganda (Soroti, Mbale, and Mulago) and one hospital in Malawi (Blantyre). The trial enrolled children aged between 2 months and 12 years admitted to hospital with severe anaemia (haemoglobin <6 g/dL). The pack type used (supplied by blood banks) was based only on availability at the time. The outcomes were haemoglobin recovery at 8 h and 180 days, requirement for retransfusion, length of hospital stay, changes in heart and respiratory rates until day 180, and the main clinical endpoints (mortality until day 28 and day 180, and readmission until day 180), measured using multivariate regression models. Findings Between Sept 17, 2014, and May 15, 2017, 3199 children with severe anaemia were enrolled into the TRACT trial. 3188 children were considered in our secondary analysis. The median age was 37 months (IQR 18–64). Whole blood was the first pack provided for 1632 (41%) of 3992 transfusions. Haemoglobin recovery at 8 h was significantly lower in those who received packed cells or settled cells than those who received whole blood, with a mean of 1·4 g/dL (95% CI –1·6 to –1·1) in children who received 30 mL/kg and –1·3 g/dL (–1·5 to –1·0) in those who received 20 mL/kg packed cells versus whole blood, and –1·5 g/dL (–1·7 to –1·3) in those who received 30 mL/kg and –1·0 g/dL (–1·2 to –0·9) in those who received 20 mL/kg settled cells versus whole blood (overall p<0·0001). Compared to whole blood, children who received blood as packed or settled cells in their first transfusion had higher odds of receiving a second transfusion (odds ratio 2·32 [95% CI 1·30 to 4·12] for packed cells and 2·97 [2·18 to 4·05] for settled cells; p<0·001) and longer hospital stays (hazard ratio 0·94 [95% CI 0·81 to 1·10] for packed cells and 0·86 [0·79 to 0·94] for settled cells; p=0·0024). There was no association between the type of blood supplied for the first transfusion and mortality at 28 days or 180 days, or readmission to hospital for any cause. 823 (26%) of 3188 children presented with severe tachycardia and 2077 (65%) with tachypnoea, but these complications resolved over time. No child developed features of confirmed cardiopulmonary overload. Interpretation Our study suggests that the use of packed or settled cells rather than whole blood leads to additional transfusions, increasing the use of a scarce resource in most of sub-Saharan Africa. These findings have substantial cost implications for blood transfusion and health services. Nevertheless, a clinical trial comparing whole blood transfusion with red cell concentrates might be needed to inform policy makers

Immediate transfusion in African children with uncomplicated severe anemia

Background The World Health Organization recommends not performing transfusions in African children hospitalized for uncomplicated severe anemia (hemoglobin level of 4 to 6 g per deciliter and no signs of clinical severity). However, high mortality and readmission rates suggest that less restrictive transfusion strategies might improve outcomes. Methods In this factorial, open-label, randomized, controlled trial, we assigned Ugandan and Malawian children 2 months to 12 years of age with uncomplicated severe anemia to immediate transfusion with 20 ml or 30 ml of whole-blood equivalent per kilogram of body weight, as determined in a second simultaneous randomization, or no immediate transfusion (control group), in which transfusion with 20 ml of whole-blood equivalent per kilogram was triggered by new signs of clinical severity or a drop in hemoglobin to below 4 g per deciliter. The primary outcome was 28-day mortality. Three other randomizations investigated transfusion volume, postdischarge supplementation with micronutrients, and postdischarge prophylaxis with trimethoprim–sulfamethoxazole. Results A total of 1565 children (median age, 26 months) underwent randomization, with 778 assigned to the immediate-transfusion group and 787 to the control group; 984 children (62.9%) had malaria. The children were followed for 180 days, and 71 (4.5%) were lost to follow-up. During the primary hospitalization, transfusion was performed in all the children in the immediate-transfusion group and in 386 (49.0%) in the control group (median time to transfusion, 1.3 hours vs. 24.9 hours after randomization). The mean (±SD) total blood volume transfused per child was 314±228 ml in the immediate-transfusion group and 142±224 ml in the control group. Death had occurred by 28 days in 7 children (0.9%) in the immediate-transfusion group and in 13 (1.7%) in the control group (hazard ratio, 0.54; 95% confidence interval [CI], 0.22 to 1.36; P=0.19) and by 180 days in 35 (4.5%) and 47 (6.0%), respectively (hazard ratio, 0.75; 95% CI, 0.48 to 1.15), without evidence of interaction with other randomizations (P>0.20) or evidence of between-group differences in readmissions, serious adverse events, or hemoglobin recovery at 180 days. The mean length of hospital stay was 0.9 days longer in the control group. Conclusions There was no evidence of differences in clinical outcomes over 6 months between the children who received immediate transfusion and those who did not. The triggered-transfusion strategy in the control group resulted in lower blood use; however, the length of hospital stay was longer, and this strategy required clinical and hemoglobin monitoring. (Funded by the Medical Research Council and Department for International Development; TRACT Current Controlled Trials number, ISRCTN84086586. opens in new tab.

Le logiciel de prédiction des défauts logiciels basé sur les graphes

Software defect prediction is one of the most explored research topics in software engineering. Modern software applications are often overly complicated and prone to failures. Software defect prediction (SDP) can alert on the risk of failure of a software component in the initial stages of development and help developers to appropriately schedule and prioritize their test efforts and ensure software quality. Traditional statistical software defect prediction tools are always time-consuming and ineffective. We argue that machine learning algorithms can capture useful properties of code that are difficult to extract by humans or other alternative research methods. However, the performance of machine learning tools varies depending on the quality of input data. Since the programming languages of modern applications hold increasingly complex characteristics which are difficult to understand, it is a prerequisite to provide a powerful representation of code analysis that can explore deeply the code software artefacts and capture useful information from different levels of abstraction of the programs. For these reasons, many efforts have been made to propose an efficient defect prediction tool, but the achievements do not represent yet high performance.In this thesis, we focus on software defect prediction and propose a novel deep learning-based technique to enhance existing defect prediction approaches. To build predictive models, previous studies focused on classic machine learning algorithms and handcrafted traditional features (i.e. software metrics). The software metrics are designed manually to capture the static properties of the code. Such methods are time-consuming and inaccurate since they fail to capture the semantic meanings of programs. Recently, researchers exploited deep learning algorithms based on either tree representations of programs or precise graphs representing program execution flows. However, these models do not offer high performance and do not cover all types of bugs. They often fail to capture intra-procedural dependencies. Indeed, several bugs are related to these dependencies. Such information is important in modelling program functionality and can lead to a more accurate defect prediction. The training procedure requires a sufficient historical data from a project to build a prediction model. Therefore, it is not practical for new projects, which have no or not enough historical data. An alternative solution is to train a prediction model by using data from other projects. The traditional approaches are based on metrics to select appropriate projects whose characteristics are close to the new project. However, the metrics are not enough to capture meaningful information from projects and then choose the best candidates that generalize well the new project. In this thesis, the emphasis was placed on two main tasks: First, to bridge the gap between programs' dependencies and defect prediction features, we propose an end-to-end deep learning algorithm to learn a powerful code representation including different levels of abstractions of code such as the syntax, the semantic and the dependencies automatically from code and further train and construct defect prediction classifier by using these complex features. The experimental results indicate that our approach can significantly improve the existing defect prediction approaches. Second, we propose a novel method to choose the best candidate projects for the project that lacks historical data. We evaluate the effectiveness of our method on 10 open-source projects. Results show that selecting carefully the projects can boost the performance of existing techniques and even of our proposed defect prediction framework, which considers all the other available projects and does not involve any selection strategy of projects.Les applications logicielles modernes sont souvent trop compliquées et sujettes aux défaillances. La prédiction des défauts logiciels alerte sur le risque de défaillance dès les premières étapes du développement et aides les développeurs à planifier leurs efforts de test et à garantir une meilleure qualité logicielle. Actuellement, les outils statistiques traditionnels de prédiction des défauts ont montré leurs limites, alors que les techniques basées sur le machine learning ont prouvé leur capacité à mieux capturer les propriétés du code. Cependant, leurs performances varient en fonction de la qualité des données d'entrée. Or, les langages et frameworks des applications modernes présentent de plus en plus de complexité, il est indispensable de fournir une représentation d'analyse de code puissante capable d'explorer en profondeur les les artefacts du code source et de capturer les informations utiles à partir des différents niveaux d'abstraction des programmes. Pour ces raisons, de nombreux travaux ont été conduits par la communauté scientifique afin de proposer de nouveaux outils de prédiction de défauts logiciels, qui sont meilleurs que les précédents, mais avec un niveau de performance qui n’est pas encore suffisant.Dans cette thèse, nous proposons une nouvelle technique de prédiction des défauts logiciels basée sur l'apprentissage en profondeur, qui améliore les approches existantes, couteuses en temps et imprécises puisqu’elles ne permettent pas d’extraire les propriétés sémantiques des programmes. Récemment, les chercheurs ont commencé à exploiter des algorithmes d'apprentissage en profondeur basés soit sur des représentations arborescentes de programmes, soit sur des représentations graphiques représentant les flux d'exécution des programmes. Bien que meilleurs, ces modèles ne permettent pas encore de couvrir tous les types de défauts tels que ceux liés aux dépendances intraprocédurales, malgré leur importance.Pour construire un modèle de prédiction efficace, il est nécessaire d’alimenter le processus d’apprentissage avec suffisamment de données historiques relatives au projet. Par conséquent, pour les projets récents ne disposant pas de données suffisantes, le modèle de prédiction doit se construire en se basant sur les données historiques d'autres projets similaires. Afin de sélectionner ces projets, les approches traditionnelles se basent sur des comparaisons statistiques. Cependant, celles-ci ne suffisent pas à capturer les différences importantes entre les projets sur plusieurs aspects tels que l'architecture, l'expérience du développeur, le style de codage, la sémantique, etc., et rendent la tâche de sélection plus complexe.Dans ce travail de thèse, le focus a été mis sur deux objectifs principaux : Premièrement, afin de combler l’écart entre les dépendances des programmes et les caractéristiques du code de prédiction des défauts, nous proposons un algorithme d'apprentissage en profondeur de bout en bout pour apprendre de manière plus complète la représentation du code source incluant différents niveaux d'abstractions tels que la syntaxe, la sémantique, les dépendances et ainsi construire un classifieur de prédiction de défauts plus performant, qui prend en compte toutes ces caractéristiques complexes. Les résultats expérimentaux montrent que notre approche améliore considérablement les approches existantes de prédiction des défauts. Deuxièmement, nous proposons une nouvelle méthode pour mieux sélectionner les projets similaires qui seront utilisés pour leurs données historiques, en se basant sur un apprentissage en profondeur des caractéristiques des projets. En évaluant notre méthode sur des projets open source, les résultats montrent qu'une sélection rigoureuse des projets améliore sensiblement les performances des techniques existantes et même notre approche de prédiction proposée, lorsqu’elle n’inclut pas de stratégie de sélection des projets externes d’apprentissage

Le logiciel de prédiction des défauts logiciels basé sur les graphes

Les applications logicielles modernes sont souvent trop compliquées et sujettes aux défaillances. La prédiction des défauts logiciels alerte sur le risque de défaillance dès les premières étapes du développement et aides les développeurs à planifier leurs efforts de test et à garantir une meilleure qualité logicielle. Actuellement, les outils statistiques traditionnels de prédiction des défauts ont montré leurs limites, alors que les techniques basées sur le machine learning ont prouvé leur capacité à mieux capturer les propriétés du code. Cependant, leurs performances varient en fonction de la qualité des données d'entrée. Or, les langages et frameworks des applications modernes présentent de plus en plus de complexité, il est indispensable de fournir une représentation d'analyse de code puissante capable d'explorer en profondeur les les artefacts du code source et de capturer les informations utiles à partir des différents niveaux d'abstraction des programmes. Pour ces raisons, de nombreux travaux ont été conduits par la communauté scientifique afin de proposer de nouveaux outils de prédiction de défauts logiciels, qui sont meilleurs que les précédents, mais avec un niveau de performance qui n’est pas encore suffisant.Dans cette thèse, nous proposons une nouvelle technique de prédiction des défauts logiciels basée sur l'apprentissage en profondeur, qui améliore les approches existantes, couteuses en temps et imprécises puisqu’elles ne permettent pas d’extraire les propriétés sémantiques des programmes. Récemment, les chercheurs ont commencé à exploiter des algorithmes d'apprentissage en profondeur basés soit sur des représentations arborescentes de programmes, soit sur des représentations graphiques représentant les flux d'exécution des programmes. Bien que meilleurs, ces modèles ne permettent pas encore de couvrir tous les types de défauts tels que ceux liés aux dépendances intraprocédurales, malgré leur importance.Pour construire un modèle de prédiction efficace, il est nécessaire d’alimenter le processus d’apprentissage avec suffisamment de données historiques relatives au projet. Par conséquent, pour les projets récents ne disposant pas de données suffisantes, le modèle de prédiction doit se construire en se basant sur les données historiques d'autres projets similaires. Afin de sélectionner ces projets, les approches traditionnelles se basent sur des comparaisons statistiques. Cependant, celles-ci ne suffisent pas à capturer les différences importantes entre les projets sur plusieurs aspects tels que l'architecture, l'expérience du développeur, le style de codage, la sémantique, etc., et rendent la tâche de sélection plus complexe.Dans ce travail de thèse, le focus a été mis sur deux objectifs principaux : Premièrement, afin de combler l’écart entre les dépendances des programmes et les caractéristiques du code de prédiction des défauts, nous proposons un algorithme d'apprentissage en profondeur de bout en bout pour apprendre de manière plus complète la représentation du code source incluant différents niveaux d'abstractions tels que la syntaxe, la sémantique, les dépendances et ainsi construire un classifieur de prédiction de défauts plus performant, qui prend en compte toutes ces caractéristiques complexes. Les résultats expérimentaux montrent que notre approche améliore considérablement les approches existantes de prédiction des défauts. Deuxièmement, nous proposons une nouvelle méthode pour mieux sélectionner les projets similaires qui seront utilisés pour leurs données historiques, en se basant sur un apprentissage en profondeur des caractéristiques des projets. En évaluant notre méthode sur des projets open source, les résultats montrent qu'une sélection rigoureuse des projets améliore sensiblement les performances des techniques existantes et même notre approche de prédiction proposée, lorsqu’elle n’inclut pas de stratégie de sélection des projets externes d’apprentissage.Software defect prediction is one of the most explored research topics in software engineering. Modern software applications are often overly complicated and prone to failures. Software defect prediction (SDP) can alert on the risk of failure of a software component in the initial stages of development and help developers to appropriately schedule and prioritize their test efforts and ensure software quality. Traditional statistical software defect prediction tools are always time-consuming and ineffective. We argue that machine learning algorithms can capture useful properties of code that are difficult to extract by humans or other alternative research methods. However, the performance of machine learning tools varies depending on the quality of input data. Since the programming languages of modern applications hold increasingly complex characteristics which are difficult to understand, it is a prerequisite to provide a powerful representation of code analysis that can explore deeply the code software artefacts and capture useful information from different levels of abstraction of the programs. For these reasons, many efforts have been made to propose an efficient defect prediction tool, but the achievements do not represent yet high performance.In this thesis, we focus on software defect prediction and propose a novel deep learning-based technique to enhance existing defect prediction approaches. To build predictive models, previous studies focused on classic machine learning algorithms and handcrafted traditional features (i.e. software metrics). The software metrics are designed manually to capture the static properties of the code. Such methods are time-consuming and inaccurate since they fail to capture the semantic meanings of programs. Recently, researchers exploited deep learning algorithms based on either tree representations of programs or precise graphs representing program execution flows. However, these models do not offer high performance and do not cover all types of bugs. They often fail to capture intra-procedural dependencies. Indeed, several bugs are related to these dependencies. Such information is important in modelling program functionality and can lead to a more accurate defect prediction. The training procedure requires a sufficient historical data from a project to build a prediction model. Therefore, it is not practical for new projects, which have no or not enough historical data. An alternative solution is to train a prediction model by using data from other projects. The traditional approaches are based on metrics to select appropriate projects whose characteristics are close to the new project. However, the metrics are not enough to capture meaningful information from projects and then choose the best candidates that generalize well the new project. In this thesis, the emphasis was placed on two main tasks: First, to bridge the gap between programs' dependencies and defect prediction features, we propose an end-to-end deep learning algorithm to learn a powerful code representation including different levels of abstractions of code such as the syntax, the semantic and the dependencies automatically from code and further train and construct defect prediction classifier by using these complex features. The experimental results indicate that our approach can significantly improve the existing defect prediction approaches. Second, we propose a novel method to choose the best candidate projects for the project that lacks historical data. We evaluate the effectiveness of our method on 10 open-source projects. Results show that selecting carefully the projects can boost the performance of existing techniques and even of our proposed defect prediction framework, which considers all the other available projects and does not involve any selection strategy of projects