Studying high impact fix-inducing changes

As software systems continue to play an important role in our daily lives, their quality is of paramount importance. Therefore, a plethora of prior research has focused on predicting components of software that are defect-prone. One aspect of this research focuses on predicting software changes that are fix-inducing. Although the prior research on fix-inducing changes has many advantages in terms of highly accurate results, it has one main drawback: It gives the same level of impact to all fix-inducing changes. We argue that treating all fix-inducing changes the same is not ideal, since a small typo in a change is easier to address by a developer than a thread synchronization issue. Therefore, in this paper, we study high impact fix-inducing changes (HIFCs). Since the impact of a change can be measured in different ways, we first propose a measure of impact of the fix-inducing changes, which takes into account the implementation work that needs to be done by developers in later (fixing) changes. Our measure of impact for a fix-inducing change uses the amount of churn, the number of files and the number of subsystems modified by developers during an associated fix of the fix-inducing change. We perform our study using six large open source projects to build specialized models that identify HIFCs, determine the best indicators of HIFCs and examine the benefits of prioritizing HIFCs. Using change factors, we are able to predict 56 % to 77 % of HIFCs with an average false alarm (misclassification) rate of 16 %. We find that the lines of code added, the number of developers who worked on a change, and the number of prior modifications on the files modified during a change are the best indicators of HIFCs. Lastly, we observe that a specialized model for HIFCs can provide inspection effort savings of 4 % over the state-of-the-art models. We believe our results would help practitioners prioritize their efforts towards the most impactful fix-inducing changes and save inspection effort.     

Studying re-opened bugs in open source software

Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs.     

Web-based algorithm for cylindricity evaluation using support vector machine learning

This paper introduces a cylindricity evaluation algorithm based on support vector machine learning with a specific kernel function, referred to as SVR, as a viable alternative to traditional least square method (LSQ) and non-linear programming algorithm (NLP). Using the theory of support vector machine regression, the proposed algorithm in this paper provides more robust evaluation in terms of CPU time and accuracy than NLP and this is supported by computational experiments. Interestingly, it has been shown that the SVR significantly outperforms LSQ in terms of the accuracy while it can evaluate the cylindricity in a more robust fashion than NLP when the variance of the data points increases. The robust nature of the proposed algorithm is expected because it converts the original nonlinear problem with nonlinear constraints into other nonlinear problem with linear constraints. In addition, the proposed algorithm is programmed using Java Runtime Environment to provide users with a Web based open source environment. In a real-world setting, this would provide manufacturers with an algorithm that can be trusted to give the correct answer rather than making a good part rejected because of inaccurate computational results.     

Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

Program diagnosis systems were developed to help users solve programming problems. By providing guidence on errors and misconceptions, these systems can help the users in writing programs and understanding their dynamic behavior. Features of software visualization which aim at providing visual and concrete depictions to the abstractions and operations of programs have also shown to be making programs more understandable. The main theme of this paper is to asses the usefulness of incorporating features of software visualization into the design of program diagnosis systems intended for novices. We report an empirical evaluation to assess the effectiveness of supporting visualization features during problem solving. The system used in the evaluation integrates visualzation and immediacy features and supports a model-tracing based approach to program diagnosis. Unlike other similar systems, our prototype system supports a more flexible style of interaction by increasing the grain size of diagnosis to a complete programming statement. The evaluation reported here seems to suggest that when supported with visualization features, systems for program diagnosis tend to be more effective in helping the users during problem solving.     

Study of inhibition effect of carboxylic salt derivative on corrosion of C1010 carbon steel in saline solution

In the present work, sodium 4-[(4-formylbenzylidene) amino] benzoiate (4) was synthesized and its structure was confirmed using spectroscopic techniques. Prepared compound was successfully applied as a corrosion inhibitor for C1010 carbon steel in 3.5% NaCl solution at 25°C. Different electrochemical measurements such as linear polarization resistance (LPR), potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS) were used to evaluate the suggested inhibitor (4). The results of different electrochemical measurements show that inhibition efficiencies obtained from EIS curves are in consistence with the results of potentiodynamic polarization and LPR measurements due to increase corrosion inhibition efficiency by increasing the concentration of organic inhibitor (4). Semi-empirical calculations with PM3 method were used to find relationship between molecular structure and inhibiting effect of suggested inhibitor (4).     

优化功率在便携式应用中的挑战

便携式产品电源管理面临挑战 便携式应用产品的外形尺寸日趋小巧轻薄，但其糅合的功能却愈来愈多。这个趋势所衍生的需求是有效管理功率及减少板上的元件数目。为了应付这个挑战，半导体供应商在这个市场区间担当了不可或缺的角色。半导体供应商必须集成最好的低功率和高功率器件，并致力在低输出功率和待机状态下实现高效率。这种集成可以通过于一块基板或平台上整合所有器件，或者把所有器件集成在一个封装里作为混合电路来实现。由于便携式产品采用的外形尺寸越来越小，开关频率因此不断增加。随着开关频率提高，无源器件的尺寸则会减小。半导体供应商需要好好管理这个高频，正是它才实现了较小型的无源器件使用，从而实现尺寸更小的总体解决方案（其成本也可能更低）。最后，2．5G和3G的RF无线器件是“线性”（linear）的，故在低功率时很容易受低效率影响。由此可见，半导体供应商必需提供能在低功率时增加系统效率的功率管理芯片。     

ACTIVE vs. PASSIVE APPROACHES TO INTELLIGENT PROGRAM DIAGNOSIS

Much of research in intelligent programming systems for users has been polarized towards two opposite domains: active and passive approaches to diagnosis. The advocates of the active approach claim that much of the effectiveness of intelligent program systems is contributed to having strong control over the behavior of the users and providing immediate feedback on errors and misconceptions. Opponents of this approach, on the other hand, have argued that active approach through its interventionist style does not provide users the flexibility needed to observe their own behavior and discover their own errors, hence the users are not given an opportunity to selfdebug their solutions. This paper covers the engineering of intelligent program diagnosis systems and reports an empirical evaluation which attempts to get some insights into the superiority of active approach over passive approach or vice versa. The evaluation is conducted using our prototype system DISCOVER. The system provides a visualization-based environment which supports both active and passive modes of intelligent program diagnosis.     

A Fast Response Impulse Voltage Measuring System for Testing of Gas Insulated Substations Equipment

The step response characteristics of a single-capacitor, differentiating-integrating (DI) measuring system are outlined. This system has an intrinsic response time less than 1 ns. The factors influencing this very fast response when the system is incorporated in a large size GIS component are discussed. Further results demonstrate the application of the system for measurements on a GIS component with steep front-chopped impulse voltage with a rate of rise up to 2 kV/ns. The very fast response and the simplicity of this system offer a powerful measuring technique for impulse testing and fast transient measurements in GIS systems.