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

A Longitudinal Study of Identifying and Paying Down Architectural Debt

Architectural debt is a form of technical debt that derives from the gap between the architectural design of the system as it "should be" compared to "as it is". We measured architecture debt in two ways: 1) in terms of system-wide coupling measures, and 2) in terms of the number and severity of architectural flaws. In recent work it was shown that the amount of architectural debt has a huge impact on software maintainability and evolution. Consequently, detecting and reducing the debt is expected to make software more amenable to change. This paper reports on a longitudinal study of a healthcare communications product created by Brightsquid Secure Communications Corp. This start-up company is facing the typical trade-off problem of desiring responsiveness to change requests, but wanting to avoid the ever-increasing effort that the accumulation of quick-and-dirty changes eventually incurs. In the first stage of the study, we analyzed the status of the "before" system, which indicated the impacts of change requests. This initial study motivated a more in-depth analysis of architectural debt. The results of this analysis were used to motivate a comprehensive refactoring of the software system. The third phase of the study was a follow-on architectural debt analysis which quantified the improvements made. Using this quantitative evidence, augmented by qualitative evidence gathered from in-depth interviews with Brightsquid's architects, we present lessons learned about the costs and benefits of paying down architecture debt in practice.Comment: Submitted to ICSE-SEIP 201

An Interactive and Dynamic Search-Based Approach to Software Refactoring Recommendations

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. On the other hand, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. Furthermore, it is difficult to merge these refactorings with other changes performed in parallel by developers. In this paper, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses NSGA-II to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. Based on this analysis, the refactorings are ranked and suggested to the developer in an interactive fashion as a sequence of transformations. The developer can approve, modify or reject each of the recommended refactorings, and this feedback is then used to update the proposed rankings of recommended refactorings. After a number of introduced code changes and interactions with the developer, the interactive NSGA-II algorithm is executed again on the new modified system to repair the set of refactoring solutions based on the new changes and the feedback received from the developer. We evaluated our approach on a set of eight open source systems and two industrial projects provided by an industrial partner. Statistical analysis of our experiments shows that our dynamic interactive refactoring approach performed significantly better than four existing search-based refactoring techniques and one fully-automated refactoring tool not based on heuristic search

Comparing Four Approaches for Technical Debt Identification

Background: Software systems accumulate technical debt (TD) when short-term goals in software development are traded for long term goals (e.g., quick-and-dirty implementation to reach a release date vs. a well-refactored implementation that supports the long term health of the project). Some forms of TD accumulate over time in the form of source code that is difficult to work with and exhibits a variety of anomalies. A number of source code analysis techniques and tools have been proposed to potentially identify the code-level debt accumulated in a system. What has not yet been studied is if using multiple tools to detect TD can lead to benefits, i.e. if different tools will flag the same or different source code components. Further, these techniques also lack investigation into the symptoms of TD "interest" that they lead to. To address this latter question, we also investigated whether TD, as identified by the source code analysis techniques, correlates with interest payments in the form of increased defect- and change-proneness. Aims: Comparing the results of different TD identification approaches to understand their commonalities and differences and to evaluate their relationship to indicators of future TD "interest". Method: We selected four different TD identification techniques (code smells, automatic static analysis (ASA) issues, grime buildup, and modularity violations) and applied them to 13 versions of the Apache Hadoop open source software project. We collected and aggregated statistical measures to investigate whether the different techniques identified TD indicators in the same or different classes and whether those classes in turn exhibited high interest (in the form of a large number of defects and higher change proneness). Results: The outputs of the four approaches have very little overlap and are therefore pointing to different problems in the source code. Dispersed coupling and modularity violations were co-located in classes with higher defect proneness. We also observed a strong relationship between modularity violations and change proneness. Conclusions: Our main contribution is an initial overview of the TD landscape, showing that different TD techniques are loosely coupled and therefore indicate problems in different locations of the source code. Moreover, our proxy interest indicators (change- and defect-proneness) correlate with only a small subset of TD indicator

Characterization of the Nucleocytoplasmic Transport Mechanisms of Epstein-Barr Virus BFLF2

Background/Aims: Epstein-Barr virus (EBV) BFLF2, the homologue of herpes simplex virus 1 (HSV-1) UL31, is crucial for the efficient viral DNA packaging and primary egress across the nuclear membrane. However, we still do not know its subcellular transport mechanisms. Methods: Interspecies heterokaryon assays were utilized to detect the nucleocytoplasmic shuttling of BFLF2, and mutation analysis, plasmid transfection and fluorescence microscopy assays were performed to identify the functional nuclear localization sequence (NLS) and nuclear export sequence (NES) of BFLF2 in live cells. Furthermore, the nuclear import and export of BFLF2 were assessed by confocal microscopy, co-immunoprecipitation and immunoblot assays. Results: BFLF2 was confirmed to shuttle between the nucleus and cytoplasm. Two predicted NESs were shown to be nonfunctional, yet we proved that the nuclear export of BFLF2 was mediated through transporter associated with antigen processing (TAP), but not chromosomal region maintenance 1 (CRM1) dependent pathway. Furthermore, one functional NLS, 22RRLMHPHHRNYTASKASAH40, was identified, and the aa22-23, aa22-25, aa28-30 and aa37-40 had an important role in the nuclear localization of BFLF2. Besides, the nuclear import of BFLF2 was demonstrated through Ran-, importin α7-, importin β1- and transportin-1-dependent mechanism that does not require importin α1, α3 and α5. Conclusion: These works are of significance for the further study of the functions of BFLF2 during EBV infection, as well as for further insights into the design of new antiviral drug target and vaccine development against EBV

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Simon: Modeling and Analysis of Design Space Structures ABSTRACT

The structure of the coupling relation on design decisions is a key factor influencing the evolvability properties and the economic value of a design. The work of Baldwin and Clark is an important step toward a theory of the relationship between structure and value. A key step to enabling rigorous validation and perhaps the eventual use of their ideas for software engineering is formalization of their model. In this paper, we present a brief overview of such a formal model and a prototype software tool, Simon, implementing it. We present Simon’s functions for deriving design structure matrices and computing impacts of changes in design decisions, and we sketch an initial experimental evaluation in the form of a replication study of our earlier analysis of Parnas’s 1972 paper on information hiding modularity