Mining the Ecosystem to Improve Type Inference For Dynamically Typed Languages

Dynamically typed languages lack information about the types of variables in the source code. Developers care about this information as it supports program comprehension. Ba- sic type inference techniques are helpful, but may yield many false positives or negatives. We propose to mine information from the software ecosys- tem on how frequently given types are inferred unambigu- ously to improve the quality of type inference for a single system. This paper presents an approach to augment existing type inference techniques by supplementing the informa- tion available in the source code of a project with data from other projects written in the same language. For all available projects, we track how often messages are sent to instance variables throughout the source code. Predictions for the type of a variable are made based on the messages sent to it. The evaluation of a proof-of-concept prototype shows that this approach works well for types that are sufficiently popular, like those from the standard librarie, and tends to create false positives for unpopular or domain specific types. The false positives are, in most cases, fairly easily identifiable. Also, the evaluation data shows a substantial increase in the number of correctly inferred types when compared to the non-augmented type inference.

Universal ontology: attentive tracking of objects and substances across languages and over development

Previous research has demonstrated that adults are successful at visually tracking rigidly moving items, but experience great difficulties when tracking substance-like ‘‘pouring’’ items. Using a comparative approach, we investigated whether the presence/absence of the grammatical count–mass distinction influences adults and children’s ability to attentively track objects versus substances. More specifically, we aimed to explore whether the higher success at tracking rigid over substance-like items appears universally or whether speakers of classifier languages (like Japanese, not marking the object–substance distinction) are advantaged at tracking substances as compared to speakers of non-classifier languages (like Swiss German, marking the object–substance distinction). Our results supported the idea that language has no effect on low-level cognitive processes such as the attentive visual processing of objects and substances. We concluded arguing that the tendency to prioritize objects is universal and independent of specific characteristics of the language spoken.

Pensionamento e plurilinguismo. Un'indagine qualitativa su immigrati italiani e su svizzeri tedeschi

Che cosa succede con le competenze linguistiche quando una persona va in pensione? Perde delle competenze perché si indebolisce il contatto con la rete sociale del lavoro o acquisisce nuove competenze grazie alla maggiore disponibilità di tempo (frequentando per esempio un corso di lingua)? Partendo da queste domande il presente lavoro indaga la tematica finora poco studiata della percezione di cambiamenti riguardo all’uso e alle competenze delle lingue durante il processo del pensionamento. Dopo aver passato in rassegna il quadro interdisciplinare degli studi incentrati sulla terza età, viene discusso il complesso di fattori che interagiscono nelle dinamiche del pensionamento. Nella seconda parte del lavoro, si presenta invece una serie di indagini empiriche svolte dall’autrice che offrono un primo approccio sociolinguistico ai mutamenti legati a questo importante momento della vita.