Specifying and detecting meaningful changes in programs

Maintaining invariant traceability through bidirectional transformations

“…Here the differences are a comprehensive representation of code rather than editing operations; 2) The EMF models of T, T , U are translated into our specific UnCal graphs using Ecore's reflection API, and as a by-product, an UnQL+ transformation is generated from the meaningful differences between T and U using the algorithm in Fig. 10 [3] 3) The UnCal graphs of T, T , U are transformed into Dot graphs by GRoundTram, which preserves the paths from the graph root to any node on the UnCal graphs in the equivalent Dot graphs; 4) Using the generated UnQL+ transformation and the Dot graphs of U as inputs, the GRoundTram system also performs a forward transformation to output a group of Dot graphs that represent T , guaranteed by the one-pass optimisation described in Section V-C; 5) Backwards, using the same UnQL+ transformation on the Dot graphs of the modified template code T , and the internal graph traceability between T and U kept by the forward transformations that performs a backward transformation to output the Dot graphs that represent the merged user code, denoted by T ⊕ U ; 6) Path-equivalent Uncal graphs corresponding to T ⊕ U ; are re-generated from the resulting Dot graphs; 7) An equivalent EMF model of those Uncal graphs for T ⊕ U is obtained using an Xtext parser * * of Uncal to process its abstract syntax using EMF API; 8) Finally, the merged Java code T ⊕U is obtained from the EMF model using JaMoPP's pretty-print function. Since we have a TXL-based parser to generate Dot graphs [3], our presented technique is not limited to EMF based external representations of Java.…”

“…Compared to existing MDD and traceability approaches, blinkit derives invariant transformations automatically from meaningful changes in the source code [3]. Unlike our initial work that proposes to apply bidirectional transformations directly on class diagrams and Java code [4], this work takes full advantages of the state-of-art synchronisations of many-to-many vertical traceability links between the model and the template code, such that the horizontal bidirectional transformations are applied only to the method bodies relevant to the user-modified behaviours.…”

The Aftermath of the Missing Flight MH370: What Can Engineers Do? [Point of View]

“…However, if the bandwidth is dedicated and shared amongst 100K operating flights, each flight could have 430bps, 25.8Kbpm (bits per minute), or 1.55Mbph (bits per hour). Since one does not need to track flights per second, and physical objects must obey the law of inertia, it is still feasible to transmit only the meaningful changes [13] frame-by-frame at a lower bandwidth. Experts deduced the possible search area for the missing MH370 through the Doppler shift effect of the received ping signals from its engine, made by Boeing 777-200ER.…”

Towards learning to detect meaningful changes in software

“…Recently we have developed an automated technique to extract meaningful parts of a program according to a programmer-defined specification [1]. The tool is based on TXL, a grammar-based source to source transformation systems developed by Cordy [4].…”

“…Specified at the language-level, we have developed an automated technique to detect only those changes that are deemed meaningful, or relevant, to a particular development task [1]. In practice, however, it is realised that programmers are not always familiar with the production rules of a programming language.…”