Un défi majeur pour les programmeurs, en particulier pour les novices, est de comprendre les messages d'erreurs émis par le compilateur. Nous nous intéresserons au problème d'affichage de bon message d'erreur de compilation. Dans certains langages, tels que Typer, la vérification du type des expressions est faite lors de la compilation; ce qui oblige le compilateur à déduire les types de certaines ou de toutes les expressions; mais aussi d'envisager la meilleure manière d'écrire le type (dans le langage source) dans un message d'erreur (ce qui est infaisable pour le moment dans Typer). Cependant l'interprétation du type des expressions faite par le compilateur est toujours différente de ce que l'utilisateur aimerait voir en cas d'erreur de compilation. En effet, lorsque le code source est converti en une représentation interne via une fonction complexe (appelée \Code{elaborate}), il peut être difficile de trouver une correspondance entre le type "t\_source" (type du code source) et le type "t\_interne" (type de la représentation interne du code source) en cas d'erreur. Parfois, "t\_source" peut ne pas être disponible ou même n'avoir jamais existé car "t\_interne" a été créé de toute pièce par inférence de type. Il peut donc être difficile de trouver un "t\_source" correspondant, d'autant plus qu'il doit être clair et compréhensible pour le programmeur. En d'autres termes, il n'existe pas d'algorithme déterministe permettant de trouver une représentation naturelle dans le code source correspondant à la représentation interne d'un type. D'où l'importance d'implémenter un système heuristique tel que les algorithmes génétiques ou les réseaux de neurones qui nous donne cette information, permettant ainsi une meilleure affichage du texte des messages d'erreurs. Nous avons donc décidé de travailler sur l'amélioration des messages d'erreur du compilateur Typer, dans sa phase de traduction du langage (interprétation et représentation des différentes expressions dans le langage source) en proposant une approche basée sur les algorithmes génétiques. \\ / A major challenge for programmers, especially for novices, is to understand the error messages issued by the compiler. We are interested in the problem of displaying correct compiler error messages. In some languages, such as Typer, the type checking of expressions is done at compile time; this forces the compiler to deduce the types of some or all expressions; but also to consider the best way to write the type (in the source language) in an error message (which is unfeasible for the moment in Typer). However, the interpretation of the type of expressions made by the compiler is always different from what the user would like to see in case of a compilation error. Indeed, when the source code is converted into an internal representation via a complex function (called ‘elaborate’), it can be difficult to find a correspondence between the type "t_source" (type of the source code) and the type "t_interne" (type of the internal representation of the source code) in case of error. Sometimes, "t_source" may not be available or even have never existed because "t_interne" was created from scratch by type inference. It can therefore be difficult to find a corresponding "source_t", especially since it must be clear and understandable for the programmer. In other words, there is no deterministic algorithm to find a natural representation in the source code corresponding to the internal representation of a type. Hence the importance of implementing a heuristic system such as genetic algorithms or neural networks that gives us this information; thus allowing a better display of the text of error messages. We therefore decided to work on the improvement of the error messages of the Typer compiler, in its language translation phase (interpretation and representation of the different expressions in the source language) by proposing an approach based on genetic algorithms.

The main aim of this study, in which 310 university students participated, is to investigate whether or not computer interfaces offering human-like apologetic error messages influence users&rsquo / self-appraisals of performances and actual performances in the computerized environment. For the study, an online instructional material which includes deliberate design problems leading to user frustration was developed. The study is comprised of three phases. In the first phase, based on the CCSARP (Cross-Cultural Study of Speech Act Realization Patterns) coding manual and the studies conducted with the framework provided by the manual, apology strategy sequences were elicited from Turkish participants. Two of these apology strategy sequences were selected for producing two apology error messages. In addition to these apology messages, one plain computer error message was also developed for experimental control. The second phase of the study was conducted to determine whether these three messages were perceived as apologies. It was found out that the two apology messages were perceived as apologies and the plain computer message was not perceived as an apology. In the third phase these three messages were used to investigate the relationship between mood, self-appraisal of performance and actual performance after the transmission of the apologetic error messages. The findings of this study show that the frequencies of apology strategies preferred in the computerized environment are similar with those utilized in the social context. Statistical analyses also reveal that the influence of apology messages on self-appraisal of performance depends on participants&rsquo / mood state and the contents of the apology messages.

This study investigates the 404 error and in what way feedback should be given to the user in an error message to generate a good user experience. To investigate this, data was gathered from 1) a literature review looking at previous studies in User Experience Design, as well as different models to evaluate ease of use and perceived usefulness, 2) a pre-study questionnaire with nine participants who were asked questions related to error messages and feedback in general, and 3) two user tests; the first including 16 participants and the second including 46 participants. During the user tests the participants interacted with a prototype of a website that included 404 error messages. In the first user test the participants' user experience was evaluated based on the TAM model, including perceived usefulness and perceived ease of use. The second user study focused more on text and illustrations and their effects on the user experience. Three versions of texts were tested, formal, apologetic and amusing. The illustrations either included a character or an object. The result showed that, in general, the users preferred formal texts, as well as illustrations that included a character. The result also suggested that there is not one type of feedback that fits all users, in order to generate a good user experience, it is important to know the audience.

In web forms, feedback can be provided to users at different points in time. This study investigates these three ways of providing feedback to find which results in the shortest completion time, which results in the lowest number of gaze revisits to input fields, and which type of feedback the users prefer. This was investigated through development of prototypes that were tested with 30 participants in a within-group design after which they were interviewed about their experiences. Providing feedback instantly or after form submission resulted in significantly shorter completion times than providing feedback after users left a field. Providing feedback instantly also resulted in significantly fewer revisits to input fields compared to providing feedback after leaving a field. Through a thematic analysis, users’ experiences were shown to be the most negative when given feedback after form submission, while the most positive experiences occurred when users were given feedback immediately. The results indicate that providing feedback immediately may be an equally good or better alternative to earlier research recommendations to provide feedback after form submission and that revisits to areas of interest may, with further research, be a measurement of optimal scanpath disruption.