Upon receiving a new issue report, practitioners start by investigating the defect type, the potential fixing effort needed to resolve the defect and the change impact. Moreover, issue reports contain valuable information, such as, the title, description and severity, and researchers leverage the topics of issue reports as a collective metric portraying similar characteristics of a defect. Nonetheless, none of the existing studies leverage the defect topic, i.e., a semantic cluster of defects of the same nature, such as Performance, GUI and Database, to estimate the change impact that represents the amount of change needed in terms of code churn and the number of files changed. To this end, in this paper, we conduct an empirical study on 298,548 issue reports belonging to three large-scale open-source systems, i.e., Mozilla, Apache and Eclipse, to estimate the change impact in terms of code churn or the number of files changed while leveraging the topics of issue reports. First, we adopt the Embedded Topic Model (ETM), a state-of-the-art topic modelling algorithm, to identify the topics. Second, we investigate the feasibility of predicting the change impact using the identified topics and other information extracted from the issue reports by building eight prediction models that classify issue reports requiring small or large change impact along two dimensions, i.e., the code churn size and the number of files changed. Our results suggest that XGBoost is the best-performing algorithm for predicting the change impact, with an AUC of 0.84, 0.76, and 0.73 for the code churn and 0.82, 0.71 and 0.73 for the number of files changed metric for Mozilla, Apache, and Eclipse, respectively. Our results also demonstrate that the topics of issue reports improve the recall of the prediction model by up to 45%.

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Given the competitive mobile app market, developers must be fullyaware of users’ needs, satisfy users’ requirements, combat apps of similar func-tionalities (i.e.,competing apps), and thus stay ahead of the competition. Whileit is easy to track the overall user ratings of competing apps, such informationfails to provide actionable insights for developers to improve their apps overthe competing apps [2]. Thus, developers still need to read reviews from alltheir interested competing apps and summarize the advantages and disadvan-tages of each app. Such a manual process can be tedious and even infeasiblewith thousands of reviews posted daily.

To help developers compare users’ opinions among competing apps onhigh-level features, such as the main functionalities and the main characteristics ofan app, we propose a review analysis approach namedFeatCompare. Feat-Compare can automatically identify high-level features mentioned in user re-views without any manually annotated resource. Then, FeatCompare creates acomparative table that summarizes users’ opinions for each identified featureacross competing apps. FeatCompare features a novel neural network-basedmodel namedGlobal-Local sensitiveFeatureExtractor (GLFE), which ex-tends Attention-based Aspect Extraction (ABAE), a state-of-the-art modelfor extracting high-level features from reviews. We evaluate the effectivenessof GLFE on 480 manually annotated reviews sampled from five groups of com-peting apps. Our experiment results show that GLFE achieves a precision of79%-82% and recall of 74%-77% in identifying the high-level features asso-ciated with reviews and outperforms ABAE by 14.7% on average. We alsoconduct a case study to demonstrate the usage scenarios of FeatCompare. A survey with 107 mobile app developers shows that more than 70% of developersagree that FeatCompare is of great benefit.

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The Timetable Problem is one of the complex problems faced in any university in the world. It is a highly-constrained combinatorial problem that seeks to find a possible scheduling for the university course offerings. There are many algorithms and approaches adopted to solve this problem, but one of the effective approaches to solve it is the use of meta-heuristics. Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. In this paper, we analyse the Genetic Algorithm approach for graph colouring corresponding to the timetable problem. The GA method is implemented in java, and the improvement of the initial solution is exhibited by the results of the experiments based on the specified constraints and requirements.

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CAN-CWiC is the premiere Canadian computing conference for Women in Technology. This year, 650 attendees, i.e., faculty members, Ph.D., MSc and undergraduate students from all across Canada, gathered in Toronto to network, learn, share and mentor women in computing.

Being surrounded by hundreds of young and professional achieved women in the tech field felt so empowering. Once on stage, I embraced the "butterflies" in my stomach and opened my talk by raising the voice advocating for women's rights around the world. I ended my speech with a piece of advice to the students: "Be comfortable with imperfections". Why imperfection? Read more about my journey of breaking free from the voice of perfection here.

I appreciate so much the teams I am fortunate to work with and the amazing leadership we have at the School of Computing! A couple of shoutouts I'd like to give to:
Prof. Hossam Hassanein, for the exemplary leadership
Prof. Mohammad Zulkernine, for the great mentorship
Debby Robertson (the Queens of the school), for the genuine and compassionate dedication to our department

About Ian A. Macleod Award

Established by friends, colleagues and students in memory of Professor Ian A. Macleod, who was a member of the Department of Computing and Information Science (currently School of Computing), from its inception in 1969 until 1995.
To commemorate his belief in the importance of a strong departmental spirit, the award is granted in the fall to the graduate student who made the greatest contribution to the intellectual and social spirit of the School of Computing during the preceding academic year.

I am excited to be selected among 50 other international researchers to serve on the MSR program committee to get first-hand experience and be trained by the leaders in the Software Engineering field. To know more about the MSR PC Shadow program, check the MSR webpage