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Digital tools and services collect a growing amount of log data. In the software development industry, such data are integral and boast valuable information on user and system behaviors with a significant potential of discovering various trends and patterns. In this study, we focus on one of those potential aspects, which is task estimation. In that regard, we perform a case study by analyzing computer recorded activities of employees from a software development company. Specifically, our purpose is to identify the task of each employee. To that end, we build a hierarchical framework with a 2-stage recognition and devise a method relying on Bayesian estimation which accounts for temporal correlation of tasks. After pre-processing, we run the proposed hierarchical scheme to initially distinguish infrequent and frequent tasks. At the second stage, infrequent tasks are discriminated between them such that the task is identified definitively. The higher performance rate of the proposed method makes it favorable against the association rule-based methods and conventional classification algorithms. Moreover, our method offers significant potential to be implemented on similar software engineering problems. Our contributions include a comprehensive evaluation of a Bayesian estimation scheme on real world data and offering reinforcements against several challenges in the data set (samples with different measurement scales, dependence characteristics, imbalance, and with insignificant pieces of information).

Disagreements in interpreting words in software requirements specifications (SRSs) can lead to project failure. Various approaches to identifying and preventing ambiguous words in SRSs have been proposed. Yet, it is unclear which ambiguous words are used in the actual SRSs and to what extent they need to be modified. This paper quantitatively analyzes existing SRSs to clarify (1) how many ambiguous words are included in SRSs and (2) how many of these words require correction. This paper targets the Request for Proposals (RFPs), which describe the initial requirements of 40 systems of local governments, libraries, universities, and hospitals in Japan. Ten ambiguous Japanese words were analyzed. The result shows that “juubun” (sufficient) appeared most frequently, and 42% required correction when this word was used. The result also indicates that the number of ambiguous words varied greatly among the RFPs and that larger RFPs did not necessarily contain more ambiguous words.

To clarify the relationship between software development productivity and the attributes of a software project, such as business area, programming language and team size, this paper analyzed 211 enterprise application development projects in Japan using a software engineering data repository established by the Software Engineering Center (SEC), Information-Technology Promotion Agency, Japan. In the analysis, we first identified factors that related to productivity based on a parallel coordinate plot (PCP) and a one-way ANOVA. An in-depth analysis on each productivity factor was then conducted by selecting a project subset for each factor so that the effect of other factors is minimized. Our findings include that the average team size was the strongest attribute relating to productivity. The outsourcing ratio (percentage), which can be controlled by software development companies, and the business sector both showed a moderate relationship to productivity. Finally, product size (FP), the duration of development and the programming language were only weakly related to productivity.

To detect the software theft, software birthmarks have been proposed. Software birthmark systems extract software birthmarks, which are native characteristics of software, from binary programs, and compare them by computing the similarity between birthmarks. This paper proposes a new procedure for scaling up the birthmark systems. While conventional birthmark systems are composed of the birthmark extraction phase and the birthmark comparison phase, the proposed method adds two new phases between extraction and comparison, namely, compression phase, which employs fuzzy hashing, and pre-comparison phase, which aims to increase distinction property of birthmarks. The proposed method enables us to reduce the required time in the comparison phase, so that it can be applied to detect software theft among many larger scale software products. From an experimental evaluation, the authors found that the proposed method significantly reduces the comparison time, and keeps the distinction performance, which is one of the important properties of the birthmark. Also, the preservation performance is acceptable when the threshold value is properly set.

To increase productivity, programmers often unwittingly violate open source software licenses by reusing code fragments, or clones. The authors explore metrics that can reveal the existence or absence of code reuse and apply these metrics to 1,225 open source product pairs.

This paper describes an empirical study to reveal factors influencing defect correction effort in software development. In the study we collected various attributes (metrics) of defects found in a typical medium-scale, multi-vendor information system development project in Japan over a six-month period. We then statistically analyzed the relationship between the defects' attributes and the correction effort. The analysis confirmed the well-known principle \"defects are the more expensive the later they are detected\" by revealing that defects detected in the \"system test\" were 4.88 times more expensive than those detected in the \"coding/unit test\". Another principle \"defects are more expensive the longer they survive in software\" was also confirmed by revealing that defects, which survived two or more development phases, were 4.44 times more expensive than those detected immediately. We also identified other factors, such as defect reproducibility, severity, and the cause of detection delay, that had a significant influence on the correction effort.

Developers must select a high-performance fault localization (FL) technique from available ones. A conventional approach is to try to select only one FL technique that is expected to attain high performance before debugging activity. In contrast, we propose a new approach that dynamically selects better FL techniques during debugging activity.

Code readability is an important indicator of software maintenance as it can significantly impact maintenance efforts. Recently, LLM (large language models) have been utilized for code readability evaluation. However, readability evaluation differs among developers, so personalization of the evaluation by LLM is needed. This study proposes a method which calibrates the evaluation, using collaborative filtering. Our preliminary analysis suggested that the method effectively enhances the accuracy of the readability evaluation using LLMs.

Cross-project defect prediction (CPDP) aims to use data from external projects as historical data may not be available from the same project. In CPDP, deciding on a particular historical project to build a training model can be difficult. To help with this decision, a Bandit Algorithm (BA) based approach has been proposed in prior research to select the most suitable learning project. However, this BA method could lead to the selection of unsuitable data during the early iteration of BA (i.e., early stage of software testing). Selecting an unsuitable model can reduce the prediction accuracy, leading to potential defect overlooking. This study aims to improve the BA method to reduce defects overlooking, especially during the early testing stages. Once all modules have been tested, modules tested in the early stage are re-predicted, and some modules are retested based on the re-prediction. To assess the impact of re-prediction and retesting, we applied five kinds of BA methods, using 8, 16, and 32 OSS projects as learning data. The results show that the newly proposed approach steadily reduced the probability of defect overlooking without degradation of prediction accuracy.