The Technical Debt Density Over Multiple Releases and the Refactoring Story

Do developers postpone fixing Technical Debt (TD) in software systems? TD is a metaphor that refers to short-term decisions in software development that may affect the cost of the software development life cycle. The bad smell is an imperfect solution in the software system that negatively impacts the internal software quality and maintainability. In this paper, we will study five open-source software projects (OSSPs) that have several releases and also estimate the numbers of architecture smells (ASs), design smells (DSs), and code smells (CSs) for every release. Designite will be used to detect smells. We describe a case study conducted to explore the following: (1) What is the average smells density for architecture, design, and code smells in an OSSP? (2) Does the density of each smell type increase over multiple releases? (3) What percentage of each smell-type density is eliminated by refactoring? We collected around 2 million LOC from five OSSPs that have multiple releases from the GitHub repository to statistically analyze the software concerning the smells as indicators of TD. We find 36% of Architecture Technical Debt (ATD) is Cyclic Dependency, while 33% of Design Debt (DD) is Cyclically-dependent Modularization. More than 70% of Code Debt (CD) is Magic Number. Even though the developers do refactoring between releases, the TD density in general increases. On average, by refactoring, developers remove around 48%, 16%, and 22% from the introduced ATD, DD, and CD from their next release, respectively.

An Empirical Study on the Architecture Instability of Software Projects

Software architecture is an artifact that expresses how the initial concept of a software system has actually been implemented. However, changes to the requirement imply continuous modification of the software system and may affect its architecture. It is expected that when a software system reaches the mature state, the requirements for evolution decrease and its architecture becomes more stable. The paper analyzes how the architecture of a software system evolves during its life cycle, with the aim of obtaining quantitative information on its possible instability after it has been declared mature. The goal is to verify if the architectural instability decreases with the increase of the software system maturity and to identify the software components that are more unstable among multiple releases. The paper proposes metrics that measure the instability of the architecture of a software system and its components through different releases. Open source software projects classified as mature and active and related historical data are analyzed. The results of the empirical study point out that the instability of software projects continues to evolve even after they are declared mature. The proposed metrics give a useful support for investigating the instability of a software project, even if further factors can be analyzed. Furthermore, the study can be replicated on other software systems belonging to different domains and developed using different programming languages.

Improving biological control of stalk borers in sugarcane by applying silicon as a soil amendment

The sugarcane stalk borers, Sesamia spp. (Lepidoptera: Noctuidae) are the most destructive sugarcane insect pests in Iran. The efficiency of Telenomus busseolae Gahan (Hymenoptera: Scelionidae) used alone or in combination with silicon fertilization was investigated for controlling the sugarcane stalk borers under field conditions. The treatments were: a combination of silicon plus multiple releases of 2,500 T. busseolae, and multiple releases of 5,000, 2,500 and 1,250 T. busseolae alone. Plots receiving no soil amendment or parasites were included as the controls. Three weeks after the first application of each treatment, 100 shoots were selected randomly from each plot and the percentage of dead heart was determined. Then, three months after the first application of parasites, the percentage of stalks damaged, the percentage of internodes bored, and the level of parasitism were determined. Finally, at harvest the percentage of stalks damaged, the percentage of internodes bored, and sugarcane quality characteristics were determined. Results indicated that the efficiency of parasitism increased when combined with an application of silicon fertilizer. The release of 2,500 T. busseolae followed by an application of silicon fertilizer decreased dead hearts to 4%, while 12% dead hearts was observed in the control plots. For the combination treatment, the percentages of stalk damage were 1.5% and 17.2%, at 3 weeks and 3 months after time release, respectively. However, the percentages of stalk damage were 35.2% and 51% when no treatment was applied. Cane quality was significantly higher with the application of silicon fertilizer plus the release of 2,500 T. busseolae, followed by releasing 5,000 Hymenoptera. The level of parasitism was also greater when parasites were released in combination with an application of silicon. We conclude that biological control by egg parasitoids can be enhanced with concurrent applications of silicon fertilizer as a soil amendment and thereby creating a more robust, Integrated Pest Management (IPM) program of stalk borers in Iranian sugarcane fields.

Smart learning: A search-based approach to rank change and defect prone classes

Research has yielded approaches for predicting future changes and defects in software artifacts, based on historical information, helping developers in effectively allocating their (limited) resources. Developers are unlikely able to focus on all predicted software artifacts, hence the ordering of predictions is important for choosing the right artifacts to concentrate on. We propose using a Genetic Algorithm (GA) for tailoring prediction models to prioritize classes with more changes/defects. We evaluate the approach on two models, regression tree and linear regression, predicting changes/defects between multiple releases of eight open source projects. Our results show that regression models calibrated by GA significantly outperform their traditional counterparts, improving the ranking of classes with more changes/defects by up to 48%. In many cases the top 10% of predicted classes can contain up to twice as many changes or defects.

Smart learning: A search-based approach to rank change and defect prone classes

Research has yielded approaches for predicting future changes and defects in software artifacts, based on historical information, helping developers in effectively allocating their (limited) resources. Developers are unlikely able to focus on all predicted software artifacts, hence the ordering of predictions is important for choosing the right artifacts to concentrate on. We propose using a Genetic Algorithm (GA) for tailoring prediction models to prioritize classes with more changes/defects. We evaluate the approach on two models, regression tree and linear regression, predicting changes/defects between multiple releases of eight open source projects. Our results show that regression models calibrated by GA significantly outperform their traditional counterparts, improving the ranking of classes with more changes/defects by up to 48%. In many cases the top 10% of predicted classes can contain up to twice as many changes or defects.

Learning multiple routes in homing pigeons

The aerial lifestyle of central-place foraging birds allows wide-ranging movements, raising fundamental questions about their remarkable navigation and memory systems. For example, we know that pigeons ( Columba livia ), long-standing models for avian navigation, rely on individually distinct routes when homing from familiar sites. But it remains unknown how they cope with the task of learning several routes in parallel. Here, we examined how learning multiple routes influences homing in pigeons. We subjected groups of pigeons to different training protocols, defined by the sequence in which they were repeatedly released from three different sites, either sequentially, in rotation or randomly. We observed that pigeons from all groups successfully developed and applied memories of the different release sites (RSs), irrespective of the training protocol, and that learning several routes in parallel did not impair their capacity to quickly improve their homing efficiency over multiple releases. Our data also indicated that they coped with increasing RS uncertainty by adjusting both their initial behaviour upon release and subsequent homing efficiency. The results of our study broaden our understanding of avian route following and open new possibilities for studying learning and memory in free-flying animals.