Analyzing Software Evolution through Feature Views

Features encapsulate the domain knowledge of a software system and thus are valuable sources of information for a reverse engineer. When analyzing the evolution of a system, we need to know how and which features were modified to recover both the change intention and its extent, namely which source artifacts are affected. Typically, the implementation of a feature crosscuts a number of source artifacts. To obtain a mapping between features to the source artifacts, we exercise the features and capture their execution traces. However this results in large traces that are difficult to interpret. To tackle this issue we compact the traces into simple sets of source artifacts that participate in a feature's runtime behavior. We refer to these compacted traces as feature views. Within a feature view, we partition the source artifacts into disjoint sets of characterized software entities. The characterization defines the level of participation of a source entity in the features. We then analyze the features over several versions of a system and we plot their evolution to reveal how and hich features were affected by changes in the code. We show the usefulness of our approach by applying it to a case study where we address the problem of merging parallel development tracks of the same system.

What sets the odds of winning and losing?

Social experience influences the outcome of conflicts such that winners are more likely to win again and losers will more likely lose again, even against different opponents. Although winner and loser effects prevail throughout the animal kingdom and crucially influence social structures, the ultimate and proximate causes for their existence remain unknown. We propose here that two hypotheses are particularly important among the potential adaptive explanations: the 'social-cue hypothesis', which assumes that victory and defeat leave traces that affect the decisions of subsequent opponents; and the 'self-assessment hypothesis', which assumes that winners and losers gain information about their own relative fighting ability in the population. We discuss potential methodologies for experimental tests of the adaptive nature of winner and loser effects.

Speech understanding in quiet and in noise with the bone-anchored hearing aids Baha Compact and Baha Divino

CONCLUSIONS: Speech understanding is better with the Baha Divino than with the Baha Compact in competing noise from the rear. No difference was found for speech understanding in quiet. Subjectively, overall sound quality and speech understanding were rated better for the Baha Divino. OBJECTIVES: To compare speech understanding in quiet and in noise and subjective ratings for two different bone-anchored hearing aids: the recently developed Baha Divino and the Baha Compact. PATIENTS AND METHODS: Seven adults with bilateral conductive or mixed hearing losses who were users of a bone-anchored hearing aid were tested with the Baha Compact in quiet and in noise. Tests were repeated after 3 months of use with the Baha Divino. RESULTS: There was no significant difference between the two types of Baha for speech understanding in quiet when tested with German numbers and monosyllabic words at presentation levels between 50 and 80 dB. For speech understanding in noise, an advantage of 2.3 dB for the Baha Divino vs the Baha Compact was found, if noise was emitted from a loudspeaker to the rear of the listener and the directional microphone noise reduction system was activated. Subjectively, the Baha Divino was rated statistically significantly better in terms of overall sound quality.