The last decades have seen the rise of many fundamental chronological debates in Old World archaeology, with far-reaching historical implications. Yet, outside of radiocarbon dating - where Bayesian formal tools and models are applied - these chronological debates are still relying on non-formal models, and dates are mostly derived by hand, without the use of mathematical or computational tools, albeit the large number of complex constraints to be taken into account. This article presents formal models and algorithms for encoding archaeologically-relevant chronological constraints, computing optimal chronologies in an automated way, and automatically checking for chronological properties of a given model.

Type inference and type reconstruction derive static types for program elements that have no static type associated with them. They have a wide range of usage, such as helping to eliminate the burden of manually specifying types, verifying whether a program is type-safe, helping to produce more optimized code or helping to understand programs. While type inference and type reconstruction is an active field of research, most existing techniques are interested foremost in the precision of their approaches, at the expense of execution speed. As a result, existing approaches are not suited to give direct feedback in development environments, where interactivity dictates very fast approaches. This paper presents a type reconstruction algorithm for variables that is extremely fast (in the order of milliseconds) and reasonably precise (75 percent). The system is implemented as a byte-code evaluator in several Smalltalk environments, and its execution speed and precision are validated on a number of concrete case studies.

Object versioning refers to how an application can have access to previous states of its objects. Implementing this mechanism is hard because it needs to be efficient in space and time, and well integrated with the programming language. This paper presents HistOOry, an object versioning system that uses an efficient data structure to store and retrieve past states. It needs only three primitives, and existing code does not need to be modified to be versioned. It provides fine-grained control over what parts of objects are versioned and when. It stores all states, past and present, in memory. Code can be executed in the past of the system and will see the complete system at that point in time. We have implemented our model in Smalltalk and used it for three applications that need versioning: checked postconditions, stateful execution tracing and a planar point location implementation. Benchmarks are provided to asses the practical complexity of our implementation.

A partially persistent data structure is a data structure which preserves previous versions of itself when it is modified. General theoretical schemes are known (e.g. the fat node method) for making any data structure partially persistent. To our knowledge however no general implementation of these theoretical methods exists to date. This paper evaluates different methods to achieve this goal and presents the first working implementation of partial persistence in the object-oriented language Java. Our approach is transparent, i.e., it allows any existing data structures to become persistent without changing its implementation where all previous solutions require an extensive modification of the code by hand. This transparent property is important in view of the large number of algorithmic results that rely on persistence. Our implementation uses aspect-oriented programming, a modularization technique which allows us to instrument the existing code with the needed hooks for the persistence implementation. The implementation is then validated by running benchmarks to analyze both the cost of persistence and of the aspect oriented approach. We also illustrate its applicability by implementing a random binary search tree and making it persistent, and then using the resulting structure to implement a point location data structure in just a few lines.