Effort Estimation of FLOSS Projects: A Study of the Linux Kernel

Empirical research on Free/Libre/Open Source Software (FLOSS) has shown that developers tend to cluster around two main roles: "core" contributors differ from "peripheral" developers in terms of a larger number of responsibilities and a higher productivity pattern. A further, cross-cutting characterization of developers could be achieved by associating developers with "time slots", and different patterns of activity and effort could be associated to such slots. Such analysis, if replicated, could be used not only to compare different FLOSS communities, and to evaluate their stability and maturity, but also to determine within projects, how the effort is distributed in a given period, and to estimate future needs with respect to key points in the software life-cycle (e.g., major releases). This study analyses the activity patterns within the Linux kernel project, at first focusing on the overall distribution of effort and activity within weeks and days;then, dividing each day into three 8-hour time slots, and focusing on effort and activity around major releases. Such analyses have the objective of evaluating effort,productivity and types of activity globally and around major releases. They enable a comparison of these releases and patterns of effort and activities with traditional software products and processes, and in turn, the identification of company-driven projects (i.e., working mainly during of?ce hours) among FLOSS endeavors. The results of this research show that, overall, the effort within the Linux kernel community is constant (albeit at different levels) throughout the week, signalling the need of updated estimation models, different from those used in traditional 9am-5pm,Monday to Friday commercial companies. It also becomes evident that the activity before a release is vastly different from after a release, and that the changes show an increase in code complexity in specific time slots (notably in the late night hours),which will later require additional maintenance efforts

Effort estimation of FLOSS projects: A study of the Linux kernel

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 SpringerEmpirical research on Free/Libre/Open Source Software (FLOSS) has shown that developers tend to cluster around two main roles: “core” contributors differ from “peripheral” developers in terms of a larger number of responsibilities and a higher productivity pattern. A further, cross-cutting characterization of developers could be achieved by associating developers with “time slots”, and different patterns of activity and effort could be associated to such slots. Such analysis, if replicated, could be used not only to compare different FLOSS communities, and to evaluate their stability and maturity, but also to determine within projects, how the effort is distributed in a given period, and to estimate future needs with respect to key points in the software life-cycle (e.g., major releases). This study analyses the activity patterns within the Linux kernel project, at first focusing on the overall distribution of effort and activity within weeks and days; then, dividing each day into three 8-hour time slots, and focusing on effort and activity around major releases. Such analyses have the objective of evaluating effort, productivity and types of activity globally and around major releases. They enable a comparison of these releases and patterns of effort and activities with traditional software products and processes, and in turn, the identification of company-driven projects (i.e., working mainly during office hours) among FLOSS endeavors. The results of this research show that, overall, the effort within the Linux kernel community is constant (albeit at different levels) throughout the week, signalling the need of updated estimation models, different from those used in traditional 9am–5pm, Monday to Friday commercial companies. It also becomes evident that the activity before a release is vastly different from after a release, and that the changes show an increase in code complexity in specific time slots (notably in the late night hours), which will later require additional maintenance efforts

RePOR: Mimicking humans on refactoring tasks. Are we there yet?

Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are `poor' solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers' performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code

A framework for the simulation of structural software evolution

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 ACM.As functionality is added to an aging piece of software, its original design and structure will tend to erode. This can lead to high coupling, low cohesion and other undesirable effects associated with spaghetti architectures. The underlying forces that cause such degradation have been the subject of much research. However, progress in this field is slow, as its complexity makes it difficult to isolate the causal flows leading to these effects. This is further complicated by the difficulty of generating enough empirical data, in sufficient quantity, and attributing such data to specific points in the causal chain. This article describes a framework for simulating the structural evolution of software. A complete simulation model is built by incrementally adding modules to the framework, each of which contributes an individual evolutionary effect. These effects are then combined to form a multifaceted simulation that evolves a fictitious code base in a manner approximating real-world behavior. We describe the underlying principles and structures of our framework from a theoretical and user perspective; a validation of a simple set of evolutionary parameters is then provided and three empirical software studies generated from open-source software (OSS) are used to support claims and generated results. The research illustrates how simulation can be used to investigate a complex and under-researched area of the development cycle. It also shows the value of incorporating certain human traits into a simulation—factors that, in real-world system development, can significantly influence evolutionary structures

Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

Similarities, challenges and opportunities of wikipedia content and open source projects

Copyright @ 2012 John Wiley & Sons, Ltd.Several years of research and evidence have demonstrated that Open Source Software (OSS) portals often contain a large amount of software projects that simply do not evolve, developed by relatively small communities, struggling to attract a sustained number of contributors. These portals have started to increasingly act as a storage for abandoned projects, and researchers and practitioners should try and point out how to take advantage of such content. Similarly, other online content portals (like Wikipedia) could be harvested for valuable content. In this paper we argue that, even with differences in the requested expertise, many projects reliant on content and contributions by users undergo a similar evolution, and follow similar patterns: when a project fails to attract contributors, it appears to be not evolving, or abandoned. Far from a negative finding, even those projects could provide valuable content that should be harvested and identified based on common characteristics: by using the attributes of “usefulness” and “modularity” we isolate valuable content in both Wikipedia pages and OSS projects

Bayesian Hierarchical Modelling for Tailoring Metric Thresholds

Software is highly contextual. While there are cross-cutting `global' lessons, individual software projects exhibit many `local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research challenge is to construct locally accurate prediction models that are informed by global characteristics and data volumes. Previous work has tackled this problem using clustering and transfer learning approaches, which identify locally similar characteristics. This paper applies a simpler approach known as Bayesian hierarchical modeling. We show that hierarchical modeling supports cross-project comparisons, while preserving local context. To demonstrate the approach, we conduct a conceptual replication of an existing study on setting software metrics thresholds. Our emerging results show our hierarchical model reduces model prediction error compared to a global approach by up to 50%.Comment: Short paper, published at MSR '18: 15th International Conference on Mining Software Repositories May 28--29, 2018, Gothenburg, Swede