Men Working in Female-Dominated Professions: A stigma or facilitation?

Gender roles not only keep men and women in different spheres of family and social life but they also promote gender segregation in the education sector and professional life. There is a lot of research being conducted on women working in male dominated professions but there is scarcity of research regarding males working in female dominated professions. This study was conducted to explore the experiences of men working in female dominated professions. The first phase of this study collected quantitative data about the type female-dominated professions in Pakistan. Based on this data in-depth qualitative interviews were done with 5 professionals using snowball sampling: Nurse, Psychologist, Montessori teacher, Makeup artist/ Beautician, and Bus host. Thematic analysis was used to identify sub-themes presented in this study: (i) Reasons or motivation for joining nontraditional profession; (ii) Reaction of near and dear ones; (iii) Positive aspects of female dominated profession; (iv) Challenges of female dominated profession; (v) Professional journey; (vi) Being a minority in female majority; (vii) Struggle to maintain masculinity and (viii) Future aspirations. Study findings can be used to support male entry and retention in female dominated professions.

Architecture Recovery of Legacy Software Systems Using Unsupervised Machine Learning Techniques

Perhaps the most important aspect in maintaining software legacy systems is un- derstanding their architecture. Architectural documentation is often unavailable. Thus efforts need to be made to recover the architectural design from the source code. This thesis addresses the problem of recovering the architecture of software systems for greater understanding, and modularizing them for greater maintainability, using machine learning techniques. We use clustering to obtain a high-level view of a software’s architecture, by identify- ing major sub-systems within it. For this purpose, we analyze the behaviour of existing similarity and distance measures when applied to software artifacts and keeping in view software characteristics, yielding explanations to some previously unanswered questions. We develop two new hierarchical clustering algorithms that address the problem of ar- bitrary decisions taken by existing hierarchical algorithms. We also propose a similarity measure suitable for software clustering. The performance of the proposed algorithms and similarity measure is evaluated using internal and external assessment. Instead of using only one expert decomposition for external assessment, as is commonly done, we use decompositions prepared by 4-5 experts for each test system. Such an approach allows us to validate the idea of multiple views of a software system. Experiments carried out on five open source legacy software systems show that the performance of our proposed algorithm is better than previously used algorithms. Interpreting the results of clustering algorithms is often difficult. To make clusters easier to understand, we propose a labeling scheme for clusters and compare two alter- native ranking schemes that can be utilized for this purpose. We demonstrate how the labels assigned by our scheme aid understanding of the clustering process of clustering algorithms. We also provide a comparison between cluster analysis and concept analysis as modularization techniques, and give examples of their application to different software structures, thus indicating the strengths and limitations of the two techniques. Finally, we use association rule mining to gain insight into the low-level structure of software systems by examining relationships between architectural quarks i.e. functions, global variables and user defined types. Metarule-guided association rule mining is used to ividentify problems within structured legacy systems. Re-engineering patterns that present solutions to these problems are proposed. Results for the test systems reveal interesting characteristics which allow us to understand legacy systems and their evolution.