By Neel Vadoothker
By Rachel Gollub
This paper presents the results of an empirical study aimed at examining the extent to which software engineers follow a software process and the extent to which they improvise during the process. Our subjects tended to classify processes into two groups. In the first group are the processes that are formal, strict, and well-documented. In the second group are the processes that are informal and not well-structured. The classification has similar characteristics to the model proposed by Truex, Baskerville, and Travis [12]. Our first group is similar to their methodical classification, and our second group is similar to their amethodical classification. Interestingly, software engineers using a process in the second group stated that they were not using a process. We believe that software engineers who think that they are not using a process, because they have the prevalent concept of process as something methodical that is strict and structured, actually are using an informal (amethodical) process. We also found that software engineers improvise while using both types of processes in order to overcome shortcomings in the planned path which arose due to unexpected situations. This finding leads us to conclude that amethodical processes are processes too.
By Rosalva E. Gallardo-Valencia, Susan Elliott Sim
Fans of PC role-playing games need no introduction to Bioware-the Edmonton, Alberta based developer of Baldur's Gate, Neverwinter Nights, and Jade Empire, among others. The company recently opened a studio in Austin, Texas to develop a massively multiplayer online role-playing game (MMORPG, or simply MMO) for an unannounced intellectual property. Ben Earhart, client technology lead on the new project, took a few hours out of his busy schedule to discuss with Crossroads the future of real-time rendering-3-D graphics that render fast enough to respond to user input, such as those required for video games.
By James Stewart
Prosodic phrasing is the means by which speakers of any given language break up an utterance into meaningful chunks. The term "prosody" itself refers to the tune or intonation of an utterance, and therefore prosodic phrases literally signal the end of one tune and the beginning of another. This study uses phrase break annotations in the Aix-MARSEC corpus of spoken English as a "gold standard" for measuring the degree of correspondence between prosodic phrases and the discrete syntactic grouping of prepositional phrases, where the latter is defined via a chunk parsing rule using nltk_lite's regular expression chunk parser.
A three-way comparison is also introduced between the "gold standard" chunk parsing rule and human judgment in the form of intuitive predictions about phrasing. Results show that even with a discrete syntactic grouping and a small sample of text, problems may arise for this rule-based method due to uncategorical behavior in parts of speech. Lack of correspondence between intuitive prosodic phrases and corpus annotations highlights the optional nature of certain boundary types. Finally, there are clear indications, supported by corpus annotations, that significant prosodic phrase boundaries occur within sentences and not just at full stops.
By Claire Brierley, Eric Atwell
By James Stewart
Static analysis tools are useful for finding common programming mistakes that often lead to field failures. However, static analysis tools regularly generate a high number of false positive alerts, requiring manual inspection by the developer to determine if an alert is an indication of a fault. The adaptive ranking model presented in this paper utilizes feedback from developers about inspected alerts in order to rank the remaining alerts by the likelihood that an alert is an indication of a fault. Alerts are ranked based on the homogeneity of populations of generated alerts, historical developer feedback in the form of suppressing false positives and fixing true positive alerts, and historical, application-specific data about the alert ranking factors. The ordering of alerts generated by the adaptive ranking model is compared to a baseline of randomly-, optimally-, and static analysis tool-ordered alerts in a small role-based health care application. The adaptive ranking model provides developers with 81% of true positive alerts after investigating only 20% of the alerts whereas an average of 50 random orderings of the same alerts found only 22% of true positive alerts after investigating 20% of the generated alerts.
By Sarah Smith Heckman
The physiology of how the human brain recalls memories is not well understood. Neural networks have been used in an attempt to model this process.
Two types of networks have been used in several models of temporal sequence memory for simple sequences of randomly generated and also of structured patterns: auto- and hetero-associative networks. Previous work has shown that a model with coupled auto- and hetero-associative continuous attractor networks can robustly recall learned simple sequences. In this paper, we compare Hebbian learning and pseudo-inverse learning in a model for recalling temporal sequences in terms of their storage capacities. The pseudo-inverse learning method is shown to have a much higher storage capacity, making the new network model 700% more efficient by reducing calculations.
By Kate Patterson
By Saman Amirpour Amraii
