While the sole purpose of undergraduate education is not to prepare the students for the industry, it is certainly one of its important objectives. In this work, we investigate how well the Bangladeshi software and IT-related undergraduate education prepares the students for the software industry. We conducted semi-structured interviews of twenty practitioners from the Bangladeshi software industry. During the interviews, these practitioners provided commentary on where they believe the undergraduate education system falls short, and provided their suggestions for improvement. Based on the themes discovered from the interviews, we created a survey where more than two hundred practitioners participated. The results of our work suggest that most of the practitioners believe that, while some aspects of the undergraduate education are fine, the undergraduate education system leaves its graduates largely unprepared for the software industry. In this paper, we summarize and present the practitioners' opinions on some key areas including but not limited to updating of syllabi, internships as part of the curricula, the nature, length and evaluation process of undergraduate projects, pedagogical issues, and academic practices.

Traditional keyboards remain the input device of choice for typing-heavy environments. When attached to sensitive data, security is a major concern. To continuously authenticate users in these environments, use of keystroke dynamics can be a preferred choice. An integral part of user enrollment in a keystroke based continuous authentication system is the writing instruction (prompt) given to the users, to use as a basis for their improvised writing. There are many prompts possible, and they directly impact the performance of authentication systems. Hence, prompts should be designed carefully, and with purpose. In this paper, we bridge the gap between cognitive psychology and computer science and attempt to influence the mental state of the users to acquire a better authentication performance. We compare two kinds of writing prompts, creative and factual, for generating reference samples. In addition, we perform two robustness tests: robustness to dissimilar writing style (e.g., creative reference and factual test) and robustness to surface (e.g., hard surface reference and soft surface test). We collect data from thirty participants in four weekly sessions. We experiment with three features: key interval, key press, and key hold latencies. We use Relative (R) measure to generate the match score between the reference and test samples. Results show that creative writing consistently performs better than the factual one. Both writing prompts perform well with dissimilar style in testing, i.e., continuous authentication is found robust to writing style. Also, we find that the surface (hard or soft) used in testing need not match that used for the reference, thus continuous authentication is also surface robust.

Benchmarks are heavily used in different areas of computer science to evaluate algorithms and tools. In program analysis and testing, open-source and commercial programs are routinely used as benchmarks to evaluate different aspects of algorithms and tools. Unfortunately, many of these programs are written by programmers who introduce different biases, not to mention that it is very difficult to find programs that can serve as benchmarks with high reproducibility of results. We propose a novel approach for generating random benchmarks for evaluating program analysis and testing tools and compilers. Our approach uses stochastic parse trees, where language grammar production rules are assigned probabilities that specify the frequencies with which instantiations of these rules will appear in the generated programs. We implemented our tool for Java and applied it to generate a set of large benchmark programs of up to 5M lines of code each with which we evaluated different program analysis and testing tools and compilers. The generated benchmarks let us independently rediscover several issues in the evaluated tools. Copyright © 2014 John Wiley & Sons, Ltd.

Test coverage is an important metric of software quality, since it indicates thoroughness of testing. In industry, test coverage is often measured as statement coverage. A fundamental problem of software testing is how to achieve higher statement coverage faster, and it is a difficult problem since it requires testers to cleverly find input data that can steer execution sooner toward sections of application code that contain more statements.

Generic repair of complex data structures is a new and exciting area of research. Existing approaches can integrate with good software engineering practices such as program assertions. But in practice there is a wide variety of assertions and not all of them satisfy the style rules imposed by existing repair techniques. I.e., a "badly" written assertion may render generic repair inefficient or ineffective. In this paper we build on the state of the art in generic repair and discuss how generic repair can work effectively with a wider range of correctness conditions.