Analyzing PL/1 Legacy Ecosystems: An Experience Report

This paper presents a case study of analyzing a legacy PL/1 ecosystem that has grown for 40 years to support the business needs of a large banking company. In order to support the stakeholders in analyzing it we developed St1-PL/1 — a tool that parses the code for association data and computes structural metrics which it then visualizes using top-down interactive exploration. Before building the tool and after demonstrating it to stakeholders we conducted several interviews to learn about legacy ecosystem analysis requirements. We briefly introduce the tool and then present results of analysing the case study. We show that although the vision for the future is to have an ecosystem architecture in which systems are as decoupled as possible the current state of the ecosystem is still removed from this. We also present some of the lessons learned during our experience discussions with stakeholders which include their interests in automatically assessing the quality of the legacy code.

Analyzing diffuse scattering with supercomputers

Two new approaches to quantitatively analyze diffuse diffraction intensities from faulted layer stacking are reported. The parameters of a probability-based growth model are determined with two iterative global optimization methods: a genetic algorithm (GA) and particle swarm optimization (PSO). The results are compared with those from a third global optimization method, a differential evolution (DE) algorithm [Storn & Price (1997). J. Global Optim. 11, 341–359]. The algorithm efficiencies in the early and late stages of iteration are compared. The accuracy of the optimized parameters improves with increasing size of the simulated crystal volume. The wall clock time for computing quite large crystal volumes can be kept within reasonable limits by the parallel calculation of many crystals (clones) generated for each model parameter set on a super- or grid computer. The faulted layer stacking in single crystals of trigonal three-pointedstar- shaped tris(bicylco[2.1.1]hexeno)benzene molecules serves as an example for the numerical computations. Based on numerical values of seven model parameters (reference parameters), nearly noise-free reference intensities of 14 diffuse streaks were simulated from 1280 clones, each consisting of 96 000 layers (reference crystal). The parameters derived from the reference intensities with GA, PSO and DE were compared with the original reference parameters as a function of the simulated total crystal volume. The statistical distribution of structural motifs in the simulated crystals is in good agreement with that in the reference crystal. The results found with the growth model for layer stacking disorder are applicable to other disorder types and modeling techniques, Monte Carlo in particular.

How do sport clubs recruit volunteers? Analyzing and developing a typology of decision-making processes on recruiting volunteers in sport clubs.

Effective strategies for recruiting volunteers who are prepared to make a long-term commitment to formal positions are essential for the survival of voluntary sport clubs. This article examines the decision-making processes in relation to these efforts. Under the assumption of bounded rationality, the garbage can model is used to grasp these decision-making processes theoretically and access them empirically. Based on case study framework an in-depth analysis of recruitment practices was conducted in nine selected sport clubs. Results showed that the decision-making processes are generally characterized by a reactive approach in which dominant actors try to handle personnel problems of recruitment in the administration and sport domains through routine formal committee work and informal networks. In addition, it proved possible to develop a typology that deliver an overview of different decision-making practices in terms of the specific interplay of the relevant components of process control (top-down vs. bottom-up) and problem processing (situational vs. systematic).

Pangea: A Workbench for Statically Analyzing Multi-Language Software Corpora

Software corpora facilitate reproducibility of analyses, however, static analysis for an entire corpus still requires considerable effort, often duplicated unnecessarily by multiple users. Moreover, most corpora are designed for single languages increasing the effort for cross-language analysis. To address these aspects we propose Pangea, an infrastructure allowing fast development of static analyses on multi-language corpora. Pangea uses language-independent meta-models stored as object model snapshots that can be directly loaded into memory and queried without any parsing overhead. To reduce the effort of performing static analyses, Pangea provides out-of-the box support for: creating and refining analyses in a dedicated environment, deploying an analysis on an entire corpus, using a runner that supports parallel execution, and exporting results in various formats. In this tool demonstration we introduce Pangea and provide several usage scenarios that illustrate how it reduces the cost of analysis.