Organizing the Technical Debt Landscape

To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subjec

Refactorings of Design Defects using Relational Concept Analysis

Software engineers often need to identify and correct design defects, ıe} recurring design problems that hinder development and maintenance\ud by making programs harder to comprehend and--or evolve. While detection\ud of design defects is an actively researched area, their correction---mainly\ud a manual and time-consuming activity --- is yet to be extensively\ud investigated for automation. In this paper, we propose an automated\ud approach for suggesting defect-correcting refactorings using relational\ud concept analysis (RCA). The added value of RCA consists in exploiting\ud the links between formal objects which abound in a software re-engineering\ud context. We validated our approach on instances of the <span class='textit'></span>Blob\ud design defect taken from four different open-source programs

Validation of the GATE Monte Carlo simulation platform for modelling a CsI(Tl) scintillation camera dedicated to small animal imaging

Monte Carlo simulations are increasingly used in scintigraphic imaging to model imaging systems and to develop and assess tomographic reconstruction algorithms and correction methods for improved image quantitation. GATE (GEANT 4 Application for Tomographic Emission) is a new Monte Carlo simulation platform based on GEANT4 dedicated to nuclear imaging applications. This paper describes the GATE simulation of a prototype of scintillation camera dedicated to small animal imaging and consisting of a CsI(Tl) crystal array coupled to a position sensitive photomultiplier tube. The relevance of GATE to model the camera prototype was assessed by comparing simulated 99mTc point spread functions, energy spectra, sensitivities, scatter fractions and image of a capillary phantom with the corresponding experimental measurements. Results showed an excellent agreement between simulated and experimental data: experimental spatial resolutions were predicted with an error less than 100 mu m. The difference between experimental and simulated system sensitivities for different source-to-collimator distances was within 2%. Simulated and experimental scatter fractions in a [98-182 keV] energy window differed by less than 2% for sources located in water. Simulated and experimental energy spectra agreed very well between 40 and 180 keV. These results demonstrate the ability and flexibility of GATE for simulating original detector designs. The main weakness of GATE concerns the long computation time it requires: this issue is currently under investigation by the GEANT4 and the GATE collaboration

On Increasing Trust Between Developers and Automated Refactoring Tools Through Visualization

In software development, maintaining good design is essential. The process of refactoring enables developers to improve this design during development without altering the program’s existing behavior. However, this process can be time-consuming, introduce semantic errors, and be difficult for developers inexperienced with refactoring or unfamiliar with a given code base. Automated refactoring tools can help not only by applying these changes, but by identifying opportunities for refactoring. Yet, developers have not been quick to adopt these tools due to a lack of trust between the developer and the tool. We propose an approach in the form of a visualization to aid developers in understanding these suggested operations and increasing familiarity with automated refactoring tools. We also provide a manual validation of this approach and identify options to continue experimentation

Handling High-Level Model Changes Using Search Based Software Engineering

Model-Driven Engineering (MDE) considers models as first-class artifacts during the software lifecycle. The number of available tools, techniques, and approaches for MDE is increasing as its use gains traction in driving quality, and controlling cost in evolution of large software systems. Software models, defined as code abstractions, are iteratively refined, restructured, and evolved. This is due to many reasons such as fixing defects in design, reflecting changes in requirements, and modifying a design to enhance existing features. In this work, we focus on four main problems related to the evolution of software models: 1) the detection of applied model changes, 2) merging parallel evolved models, 3) detection of design defects in merged model, and 4) the recommendation of new changes to fix defects in software models. Regarding the first contribution, a-posteriori multi-objective change detection approach has been proposed for evolved models. The changes are expressed in terms of atomic and composite refactoring operations. The majority of existing approaches detects atomic changes but do not adequately address composite changes which mask atomic operations in intermediate models. For the second contribution, several approaches exist to construct a merged model by incorporating all non-conflicting operations of evolved models. Conflicts arise when the application of one operation disables the applicability of another one. The essence of the problem is to identify and prioritize conflicting operations based on importance and context – a gap in existing approaches. This work proposes a multi-objective formulation of model merging that aims to maximize the number of successfully applied merged operations. For the third and fourth contributions, the majority of existing works focuses on refactoring at source code level, and does not exploit the benefits of software design optimization at model level. However, refactoring at model level is inherently more challenging due to difficulty in assessing the potential impact on structural and behavioral features of the software system. This requires analysis of class and activity diagrams to appraise the overall system quality, feasibility, and inter-diagram consistency. This work focuses on designing, implementing, and evaluating a multi-objective refactoring framework for detection and fixing of design defects in software models.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136077/1/Usman Mansoor Final.pdfDescription of Usman Mansoor Final.pdf : Dissertatio

The cleanroom case study in the Software Engineering Laboratory: Project description and early analysis

This case study analyzes the application of the cleanroom software development methodology to the development of production software at the NASA/Goddard Space Flight Center. The cleanroom methodology emphasizes human discipline in program verification to produce reliable software products that are right the first time. Preliminary analysis of the cleanroom case study shows that the method can be applied successfully in the FDD environment and may increase staff productivity and product quality. Compared to typical Software Engineering Laboratory (SEL) activities, there is evidence of lower failure rates, a more complete and consistent set of inline code documentation, a different distribution of phase effort activity, and a different growth profile in terms of lines of code developed. The major goals of the study were to: (1) assess the process used in the SEL cleanroom model with respect to team structure, team activities, and effort distribution; (2) analyze the products of the SEL cleanroom model and determine the impact on measures of interest, including reliability, productivity, overall life-cycle cost, and software quality; and (3) analyze the residual products in the application of the SEL cleanroom model, such as fault distribution, error characteristics, system growth, and computer usage