Optimizing compilers often sacrifice binary size in pursuit of higher run-time performance. In the absence of method execution profiles, they uniformly apply performance-oriented optimizations, typically various forms of code duplication. Duplications in methods that are rarely or never executed only increase binary size without improving performance. Modern static profiler use ML to predict branch profiles, yet they do not identify which methods will be frequently executed at run time. Doing so would enable more selective optimizations, reducing binary size while preserving or only minimally affecting run-time performance.

Context: Computer science, even now, attracts a small number of women, and the proportion of women in the field decreases through advancing career stages. Consequently, few women progress to Ph.D. studies in computer science after completing master’s studies. Empowering women at this stage in their careers is essential, not just for equality reasons, but to unlock untapped potential for society, industry and academia. Objective: This paper aims to identify students’ career assumptions and information related to Ph.D. studies focused on gender-based differences. We propose a program to inform female master students about Ph.D. studies that explains the process, clarifies misconceptions, and alleviates concerns. Method: An extensive survey was conducted to identify factors that encourage and discourage students from undertaking Ph.D. studies. The analysis identified statistically significant differences between those who undertook Ph.D. studies and those who did not, as well as statistically significant gender differences. A catalogue of questions to initiate discussions with potential Ph.D. students which allowed them to explore these factors was developed. These were structured into a Women’s Career Lunch program where students can explore and discuss the benefits of Ph.D. study. Results: Encouraging factors towards Ph.D. study include interest and confidence in research arising from a research involvement during earlier studies; enthusiasm for and self-confidence in computer science in addition to an interest in an academic career; encouragement from external sources; and a positive perception towards Ph.D. studies which can involve achieving personal goals. Discouraging factors include uncertainty and lack of knowledge of the Ph.D. process, a perception of lower job flexibility, and the requirement for long-term commitment. Gender differences highlighted that female students who pursue a Ph.D. have less confidence in their technical skills than males but a higher preference for interdisciplinary areas. Female students are less inclined than males to perceive the industry as offering better job opportunities and more flexible career paths than academia. Conclusions: The insights collected from the survey facilitated the development of a questions catalogue structured into the Women Career Lunch program to help students make a more informed decision concerning whether they should pursue a Ph.D. in computer science. Localised versions of this program, in 8 languages, were created to support its adoption in different countries and assist in mitigating the female under-representation challenge.

Solving the sparql query containment problem is of fundamental importance for the verification and optimization of sparql queries. With the increasing popularity of the Semantic Web and its applications, sparql query containment solvers face significant challenges: covering a wide range of language constructs, achieving high efficiency, and guaranteeing correctness. While language coverage and efficiency can be reliably evaluated by testing with relevant benchmarks, we need formal proof of correctness to ensure the trustworthiness of a tool.In this paper, we prove the correctness of SpeCS a highly efficient state-of-the-art query containment solver that supports reasoning about queries containing all commonly used sparql language constructs. We outline set semantics that cover the most common subset of the sparql language and give precise definitions of all fundamental sparql concepts. We briefly discuss the procedure used by SpeCS for reducing the query containment problem into a formal logical framework. We prove that this procedure is both sound and complete for conjunctive queries as well as for some important classes of non-conjunctive queries (queries containing the union operator, the optional operator, and subqueries). We consider soundness and completeness in both containment and subsumption forms. We also discuss the advantages of solver development driven by correctness proofs.

Automated and reliable software verification is of crucial importance for development of high-quality software. Formal methods can be used for finding different kinds of bugs without executing the software, for example, for finding possible run-time errors. The methods like model checking and symbolic execution offer very precise static analysis but on real world programs do not always scale well. One way to tackle the scalability problem is to apply new concurrent and sequential approaches to complex algorithms used in these kinds of software analysis. In this paper, we compare different variants of bounded model checking and propose two concurrent approaches: concurrency of intra-procedural analysis and concurrency of inter-procedural analysis. We implemented these approaches in a software verification tool LAV, a tool that is based on bounded model checking and symbolic execution. For assessing the improvements gained, we experimentally compared the concurrent approaches with the standard bounded model checking approach (where all correctness conditions are put into a single compound formula) and with a sequential approach (where correctness conditions are checked separately, one after the other). The results show that, in many cases, the proposed concurrent approaches give significant improvements.